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Dudczyk, Janusz, e Łukasz Rybak. "Application of Data Particle Geometrical Divide Algorithms in the Process of Radar Signal Recognition". Sensors 23, n.º 19 (30 de setembro de 2023): 8183. http://dx.doi.org/10.3390/s23198183.
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The process of recognising and classifying radar signals and their radiation sources is currently a key element of operational activities in the electromagnetic environment. Systems of this type, called ELINT class systems, are passive solutions that detect, process, and analyse radio-electronic signals, providing distinctive information on the identified emission source in the final stage of data processing. The data processing in the mentioned types of systems is a very sophisticated issue and is based on advanced machine learning algorithms, artificial neural networks, fractal analysis, intra-pulse analysis, unintentional out-of-band emission analysis, and hybrids of these methods. Currently, there is no optimal method that would allow for the unambiguous identification of particular copies of the same type of radar emission source. This article constitutes an attempt to analyse radar signals generated by six radars of the same type under comparable measurement conditions for all six cases. The concept of the SEI module for the ELINT system was proposed in this paper. The main aim was to perform an advanced analysis, the purpose of which was to identify particular copies of those radars. Pioneering in this research is the application of the author’s algorithm for the data particle geometrical divide, which at the moment has no reference in international publication reports. The research revealed that applying the data particle geometrical divide algorithms to the SEI process concerning six copies of the same radar type allows for almost three times better accuracy than a random labelling strategy within approximately one second.
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Xing, Huaixi, Qinghua Xing e Kun Wang. "Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach". Aerospace 10, n.º 3 (27 de fevereiro de 2023): 236. http://dx.doi.org/10.3390/aerospace10030236.
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Current electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of radar intelligent anti-jamming systems are analyzed, and a scheduling method for radar anti-jamming action based on the Partially Observable Markov Process (POMDP) is proposed. The sample-based belief distribution is used to reflect the radar’s cognition of the environment and describes the uncertainty of the recognition of jamming patterns in the belief state space. The belief state of jamming patterns is updated with Bayesian rules. The reward function is used as the evaluation criterion to select the best anti-jamming strategy, so that the radar is in a low threat state as often as possible. Numerical simulation combines the behavioral prior knowledge base of radars and jammers and obtains the behavioral confrontation benefit matrix from the past experience of experts. The radar controls the output according to the POMDP policy, and dynamically performs the best anti-jamming action according to the change of jamming state. The results show that the POMDP anti-jamming policy is better than the conventional policy. The POMDP approach improves the adaptive anti-jamming capability of the radar and can quickly realize the anti-jamming decision to jammers. This work provides some design ideas for the subsequent development of an intelligent radar.
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Sun, Jingming, Qiang Zhang, Jingbei Yang e Yuhao Yang. "Automatic Sample Labeling Method for Radar Target Recognition". Journal of Physics: Conference Series 2356, n.º 1 (1 de outubro de 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2356/1/012029.
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Rapid and accurate recognition of target types is one of the key problems that must be solved to improve radar detection and perception performance. However, the existing radar target recognition technology faces the problems of low efficiency and low quality of sample labeling in practical application. Aiming at the urgent need of automatic sample labeling in radar target recognition, this paper proposes an automatic sample labeling method based on the matching of auxiliary source information and radar track information, which can effectively improve the efficiency and quality of sample labeling in radar target recognition. The experimental results show that the proposed method can effectively label the wide and narrow band data samples required for radar target recognition, and has the advantages of automatic process and high labeling accuracy.
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Bartsch, A., F. Fitzek e R. H. Rasshofer. "Pedestrian recognition using automotive radar sensors". Advances in Radio Science 10 (18 de setembro de 2012): 45–55. http://dx.doi.org/10.5194/ars-10-45-2012.
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Abstract. The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.
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Vinogradova, N. S., e L. G. Dorosinsky. "Recognition of radar images generated by synthetic aperture radar systems". Ural Radio Engineering Journal 5, n.º 3 (2021): 258–71. http://dx.doi.org/10.15826/urej.2021.5.3.004.
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In the field of radar remote sensing of the Earth, the problem of detecting and / or identifying spatially distributed targets against the background of a homogeneous surface is becoming increasingly important, for example, the tasks of the coast guard, monitoring of unauthorized forest logging, assessing the consequences of natural disasters, and others. This study is devoted to solving the problem of developing the optimal algorithm for making a decision on the class of a spatially distributed target based on data from side-scan radar systems with a synthetic aperture. A detailed description of the signal formation process in the fixed range channel is given, taking into account possible interference factors. Based on the statistical criterion by the method of maximum likelihood, the recognition algorithm is proposed, expressions for the formation of a feature vector are obtained, and the nonparametric decision rule is proposed. The algorithm has been tested on the example of recognizing three classes of spatially distributed targets that differ in size.
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Lee, Gawon, e Jihie Kim. "Improving Human Activity Recognition for Sparse Radar Point Clouds: A Graph Neural Network Model with Pre-Trained 3D Human-Joint Coordinates". Applied Sciences 12, n.º 4 (18 de fevereiro de 2022): 2168. http://dx.doi.org/10.3390/app12042168.
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Many devices have been used to detect human action, including wearable devices, cameras, lidars, and radars. However, some people, such as the elderly and young children, may not know how to use wearable devices effectively. Cameras have the disadvantage of invading privacy, and lidar is rather expensive. In contrast, radar, which is widely used commercially, is easily accessible and relatively cheap. However, due to the limitations of radio waves, radar data are sparse and not easy to use for human activity recognition. In this study, we present a novel human activity recognition model that consists of a pre-trained model and graph neural networks (GNNs). First, we overcome the sparsity of the radar data. To achieve that, we use a model pre-trained with the 3D coordinates of radar data and Kinect data that represents the ground truth. With this pre-trained model, we extract reliable features as 3D human joint coordinate estimates from sparse radar data. Then, a GNN model is used to extract additional information in the spatio-temporal domain from these joint coordinate estimates. Our approach was evaluated using the MMActivity dataset, which includes five different human activities. Our system achieved an accuracy of 96%. The experimental result demonstrates that our algorithm is more effective than five other baseline models.
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Dong, Xiaoxuan, e Siyi Cheng. "Radar Working Modes Recognition Based on Discrete Process Neural Network". IOP Conference Series: Materials Science and Engineering 394 (8 de agosto de 2018): 042088. http://dx.doi.org/10.1088/1757-899x/394/4/042088.
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Yang, Rui, Yingbo Zhao e Yuan Shi. "RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification". Applied Sciences 13, n.º 22 (20 de novembro de 2023): 12511. http://dx.doi.org/10.3390/app132212511.
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When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods.
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Feng, Xiang, Zhengliang Shan, Zhanfeng Zhao, Zirui Xu, Tianpeng Zhang, Zihe Zhou, Bo Deng e Zirui Guan. "Millimeter-Wave Radar Monitoring for Elder’s Fall Based on Multi-View Parameter Fusion Estimation and Recognition". Remote Sensing 15, n.º 8 (16 de abril de 2023): 2101. http://dx.doi.org/10.3390/rs15082101.
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Human activity recognition plays a vital role in many applications, such as body falling surveillance and healthcare for elder’s in-home monitoring. Instead of using traditional micro-Doppler signals based on time-frequency distribution, we turn to another way and use the Relax algorithm to process the radar echo so as to obtain the required parameters. In this paper, we aim at the multi-view idea in which two radars at different views work synchronously and fuse the features extracted from each radar, respectively. Furthermore, we discuss the common estimated time-frequency features and time-varying spatial features of multi-view radar-echo and then formulate the parameters matrix via principal component analysis, and finally transform them into the machine learning classifiers to make further comparisons. Simulations and results show that our proposed multi-view parameter fusion idea could lead to relative-high accuracy and robust recognition performance, which would provide a feasible application for future human–computer monitoring scenarios.
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Zhyrnov, V., e S. Solonska. "Intelligent model of radar object images for surveillance radars". Radiotekhnika, n.º 212 (28 de março de 2023): 148–54. http://dx.doi.org/10.30837/rt.2023.1.212.14.
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The results of developing an intelligent model of radar object images for surveillance radars are presented. The relevance of this work deals with the development of algorithm for automatic processing images of radar objects that provide effective detection of weak true signals due to the accumulation of signal and logical information in the analyzed cell and in its surroundings under interferences. The improvement of air safety tools and the automation of air traffic management processes require effective procedures to process signal information. The issues of more complete use and qualitative improvement of the information-processing capabilities of control systems are also topical, especially in difficult conditions of interfering signals. The basis of this study is the idea of using an intellectual model of radar object images for automatic decision-making on detection and recognition of radar objects, built on the space of semantic features. The main result is optical object recognition, similar to how an expert can easily recognize aerial objects and their types when viewing radar object images. Based on semantic features intelligent model of radar object images has been developed, which makes it possible to effectively detect and classify aerial objects. It is worth noting that the characteristic description of intelligent model of radar object images for point, extended, moving and stationary radar objects is the mathematical description of procedures and relationships at perception and analysis of signals in the form of distinguishing features or properties. As a result, various virtual images of radar object are generated in the form of spatial-semantic and spectral-semantic models. The main features and structural elements of the model are given. It is shown that the advantages of this model are related to the possibility of characteristic description of the radar object images using the algebra of finite predicates.
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Zhang, Tongrui, e Yunsheng Fan. "A 3D U-Net Based on a Vision Transformer for Radar Semantic Segmentation". Sensors 23, n.º 24 (5 de dezembro de 2023): 9630. http://dx.doi.org/10.3390/s23249630.
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Radar data can be presented in various forms, unlike visible data. In the field of radar target recognition, most current work involves point cloud data due to computing limitations, but this form of data lacks useful information. This paper proposes a semantic segmentation network to process high-dimensional data and enable automatic radar target recognition. Rather than relying on point cloud data, which is common in current radar automatic target recognition algorithms, the paper suggests using a radar heat map of high-dimensional data to increase the efficiency of radar data use. The radar heat map provides more complete information than point cloud data, leading to more accurate classification results. Additionally, this paper proposes a dimension collapse module based on a vision transformer for feature extraction between two modules with dimension differences during dimension changes in high-dimensional data. This module is easily extendable to other networks with high-dimensional data collapse requirements. The network’s performance is verified using a real radar dataset, showing that the radar semantic segmentation network based on a vision transformer has better performance and fewer parameters compared to segmentation networks that use other dimensional collapse methods.
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Milczarek, Hubert, Czesław Leśnik, Igor Djurović e Adam Kawalec. "Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study". Sensors 21, n.º 8 (17 de abril de 2021): 2840. http://dx.doi.org/10.3390/s21082840.
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Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.
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Díez-Pastor, José Francisco, Pedro Latorre-Carmona, José Luis Garrido-Labrador, José Miguel Ramírez-Sanz e Juan J. Rodríguez. "Experimental Assessment of Feature Extraction Techniques Applied to the Identification of Properties of Common Objects, Using a Radar System". Applied Sciences 11, n.º 15 (22 de julho de 2021): 6745. http://dx.doi.org/10.3390/app11156745.
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Radar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user’s hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance.
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Qu, Chongxiao, Yongjin Zhang, Lei Jin, Changjun Fan, Shuo Liu e Xiayan Chen. "Exploring hand gesture recognition using micro-Doppler radar data based on vision transformers". Journal of Physics: Conference Series 2504, n.º 1 (1 de maio de 2023): 012046. http://dx.doi.org/10.1088/1742-6596/2504/1/012046.
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Abstract Nowadays, radar technology is undergoing rapid development. Contrary to the old stereotype of radar systems being large, complex and mainly used for detecting targets at a long distance, small and compact radio frequency products are getting flourishing and widely applied. Benefiting from this, hand gesture recognition using a low-cost, low-power consuming radar is becoming an area of wide interest and study. In this paper, we explore using low-cost FMCW (Frequency Modulated Continuous Wave) radar modules for gesture recognition based on vision transformers. First, we pre-process the radar data and convert the 1-dimensional spectrogram complex samples into 2-dimensional matrices just like images. Then, we adopt an existing model based on vision transformers to classify them, which leverages existing state-of-the-art technologies for vision recognition problems to address our issue. Experimental results show that our scheme yields good performance and it is a promising method.
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Jiang, Xinrui, Ye Zhang, Qi Yang, Bin Deng e Hongqiang Wang. "Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network". Sensors 20, n.º 19 (23 de setembro de 2020): 5466. http://dx.doi.org/10.3390/s20195466.
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At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.
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Denisenkov, D. A., V. Yu Zhukov e G. G. Shchukin. "Spectral Parameters of Signal in a Meteorological Radar". Радиотехника и электроника 68, n.º 6 (1 de junho de 2023): 621–24. http://dx.doi.org/10.31857/s0033849423060013.
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Specific features of the application of the spectral parameters of the received signal for the recognition of dangerous weather phenomena in a meteorological radar are considered. The advantages and disadvantages of existing methods are outlined. A new recognition method based on the estimation of the base of the input random process is proposed.
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Ning, Qianhao, Hongyuan Wang, Zhiqiang Yan, Xiang Liu e Yinxi Lu. "Space-Based THz Radar Fly-Around Imaging Simulation for Space Targets Based on Improved Path Tracing". Remote Sensing 15, n.º 16 (13 de agosto de 2023): 4010. http://dx.doi.org/10.3390/rs15164010.
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Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff’s approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future.
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Sha, Minghui, Dewu Wang, Fei Meng, Wenyan Wang e Yu Han. "Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition". Future Internet 15, n.º 12 (23 de novembro de 2023): 374. http://dx.doi.org/10.3390/fi15120374.
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With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from −16 dB to −8 dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from −16 dB to −8 dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments.
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Wan, Jian, Xin Yu e Qiang Guo. "LPI Radar Waveform Recognition Based on CNN and TPOT". Symmetry 11, n.º 5 (27 de maio de 2019): 725. http://dx.doi.org/10.3390/sym11050725.
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The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.
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Ukhanov, E. V. Ukhanov. "SOLVING THE PROBLEM OF OPTIMAL RADAR RECOGNITION OF MOBILE AERIAL OBJECTS BASED ON THE THEORY OF STATISTICAL HYPOTHESIS TESTING". T-Comm 16, n.º 11 (2022): 30–34. http://dx.doi.org/10.36724/2072-8735-2022-16-11-30-34.
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The materials of this article are devoted to the development of one of the main aspects of artificial intelligence systems - pattern recognition. The relevance of the materials is due to the rapid development of radar systems for various purposes and the transition in some directions from radar to radio vision. Currently, much attention is paid to the development of radar systems with synthesizing the antenna aperture, for remote sensing of the earth and recognition of stationary ground objects, however, according to the author, radar recognition of mobile aerial objects is an important issue. The purpose of this article is to propose a solution to the problem of recognizing moving aerial objects by their radar portraits, based on the theory of statistical hypothesis testing. At the moment there are many methods of pattern recognition, this article discusses an algorithm that implements the function of matching the current image and the reference from a pre-formed catalog. As the current image, an azimuth-range radar portrait is considered, which is formed by super-resolution in azimuth, by synthesizing the aperture of the antenna and in range, using ultra-wideband signal. The author suggests, with a statistical approach to solving the problem of radar recognition, not to be tied to finding the probability of an object belonging to each of the pre-formed classes using a selected feature with a known probability distribution density of values, but to consider this process from the position of the signal at the output of the optimal recognition system to a specific image. A new approach to the description of probabilistic events when making a recognition decision is proposed. As a statistical classifier, it is proposed to use the Neumann-Pearson theory of statistical solutions.
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Yang, Na, e Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images". Scientific Programming 2022 (20 de janeiro de 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.
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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.
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Wang, Zihao, Haifeng Li e Lin Ma. "Modern Synergetic Neural Network for Synthetic Aperture Radar Target Recognition". Sensors 23, n.º 5 (4 de março de 2023): 2820. http://dx.doi.org/10.3390/s23052820.
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Feature extraction is an important process for the automatic recognition of synthetic aperture radar targets, but the rising complexity of the recognition network means that the features are abstractly implied in the network parameters and the performances are difficult to attribute. We propose the modern synergetic neural network (MSNN), which transforms the feature extraction process into the prototype self-learning process by the deep fusion of an autoencoder (AE) and a synergetic neural network. We prove that nonlinear AEs (e.g., stacked and convolutional AE) with ReLU activation functions reach the global minimum when their weights can be divided into tuples of M-P inverses. Therefore, MSNN can use the AE training process as a novel and effective nonlinear prototypes self-learning module. In addition, MSNN improves learning efficiency and performance stability by making the codes spontaneously converge to one-hots with the dynamics of Synergetics instead of loss function manipulation. Experiments on the MSTAR dataset show that MSNN achieves state-of-the-art recognition accuracy. The feature visualization results show that the excellent performance of MSNN stems from the prototype learning to capture features that are not covered in the dataset. These representative prototypes ensure the accurate recognition of new samples.
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Liu, Jie, Kai Zhang, Zhenlin Sun, Qiang Wu, Wei He e Hao Wang. "Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar". Applied Sciences 11, n.º 19 (24 de setembro de 2021): 8926. http://dx.doi.org/10.3390/app11198926.
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At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and synthetic aperture radar techniques are used to obtain the raw data. The analytical Fourier transform algorithm is used for image reconstruction. When imaging a target at 90 mm from radar, which belongs to the near field imaging scene, the image resolution can reach 1.90 mm in X-direction and 1.73 mm in Y-direction. Since the error caused by the distance between radar and target will lead to noise, the original reconstruction image is processed by gamma transform, which eliminates image noise, then the image is enhanced by linearly stretched transform to improve visual recognition, which lays a good foundation for supervised learning. In order to flexibly deploy the machine learning algorithm in various application scenarios, ShuffleNetV2, MobileNetV3 and GhostNet representative of lightweight convolutional neural networks with redefined convolution, branch structure and optimized network layer structure are used to distinguish multi-category SAR images. Through the fusion of squeeze-and-excitation and the selective kernel attention mechanism, more precise features are extracted for classification, the proposed GhostNet_SEResNet56 can realize the best classification accuracy of SAR images within limited resources, which prediction accuracy is 98.18% and the number of parameters is 0.45 M.
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Banasiak, Kazimierz. "Selected aspects of measurement data processing in electronic warfare devices". Bulletin of the Military University of Technology 72, n.º 3 (30 de setembro de 2023): 83–119. http://dx.doi.org/10.5604/01.3001.0054.6451.
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Electronic warfare (Polish acronym WRE ‒ walka radioelektroniczna) is a set of militaryactions based on the use of the electromagnetic spectrum and it includes:— reception and identification of electromagnetic emissions,— reducing the effectiveness of the enemy’s electronic devices,— enabling effective use of the spectrum by own forces.The task of the WRE in peacetime is to obtain information about radio-electronic devices, especiallyradars. These tasks are performed by ELINT (Electronic Intelligence) and/or ESM (Electronic WarfareSupport Measures) devices. They operate passively in the 0.5÷18 GHz band, their detection is difficultand the information they provide allows to detect a threat to own forces and to take appropriatecountermeasures. Radar recognition is a complex process that involves the following stages. The firststage: gathering measurements and their grouping into the so-called packets and graphical display ofthe received pulse trains. The measurements are represented by the so-called measurement vectors(descriptors) containing signal parameters, including the TOA (Time of Arrival) pulse with nanosecondaccuracy. The second stage: associating pulse packets with logical sources in order to obtainrepresentative data strings. This stage is signal sorting and deinterleaving. The third stage: estimationof the WS signal vector based on the associated pulse packets. The final stage of data processing is thecomparison of the obtained signal descriptor parameters WS with Database (BD) radar patterns. Thereconnaissance result allows for the assessment of the threat resulting from the operation of this radar.Radar recognition requires precise determination of many parameters of its signal. For pulsed radars,important parameters are the Pulse Repetition Interval (PRI) and the type of PRI modulation. Radarsare characterised by a high complexity of PRI changes. This provides great utility and makes it possibleto distinguish even radar units and changes in their locations. This allows for the detection and fullidentification of objects that use radars (e.g. ships) with the given object completely out of sight; it alsoallows to predict intentions of the target. The basic inter-pulse PRI modulations include: stagger, dwelland switch, jitter or sliding. PRI changes in radar signals may be periodic with long cycles, difficultto identify in cluttered conditions. Unfortunately, the received pulse trains are usually distorted. Thismakes the WS estimation process difficult ‒ especially in automatic mode. PRI analysis has receivedmuch attention in the literature. The methods are computationally complex and have numerouslimitations. This article presents ASWC algorithm (Sequential Cycle Detection Algorithm) of PRIproprietary, with relatively low computational complexity. Examples of test results confirming its higheffectiveness in automatic PRI analysis performed under interference conditions were also presented.Keywords: radars, electronic warfare, radar recognition, ELINT, ESM, pulse descriptor, PRI
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Chen, Yingchao, Peng Li, Erxing Yan, Zehuan Jing, Gaogao Liu e Zhao Wang. "A Knowledge Graph-Driven CNN for Radar Emitter Identification". Remote Sensing 15, n.º 13 (27 de junho de 2023): 3289. http://dx.doi.org/10.3390/rs15133289.
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In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method.
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Dudczyk, Janusz. "Radar Emission Sources Identification Based on Hierarchical Agglomerative Clustering for Large Data Sets". Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1879327.
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More advanced recognition methods, which may recognize particular copies of radars of the same type, are called identification. The identification process of radar devices is a more specialized task which requires methods based on the analysis of distinctive features. These features are distinguished from the signals coming from the identified devices. Such a process is called Specific Emitter Identification (SEI). The identification of radar emission sources with the use of classic techniques based on the statistical analysis of basic measurable parameters of a signal such as Radio Frequency, Amplitude, Pulse Width, or Pulse Repetition Interval is not sufficient for SEI problems. This paper presents the method of hierarchical data clustering which is used in the process of radar identification. The Hierarchical Agglomerative Clustering Algorithm (HACA) based on Generalized Agglomerative Scheme (GAS) implemented and used in the research method is parameterized; therefore, it is possible to compare the results. The results of clustering are presented in dendrograms in this paper. The received results of grouping and identification based on HACA are compared with other SEI methods in order to assess the degree of their usefulness and effectiveness for systems of ESM/ELINT class.
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Deng, Jie, e Fulin Su. "SDRnet: A Deep Fusion Network for ISAR Ship Target Recognition Based on Feature Separation and Weighted Decision". Remote Sensing 16, n.º 11 (27 de maio de 2024): 1920. http://dx.doi.org/10.3390/rs16111920.
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Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition.
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Guo, Li Rong, Ming Hao He, Chun Lai Yu e Bing Qie Wang. "A New Method of Time Domain Coherency for Radar Emitter Signal Sorting". Advanced Materials Research 981 (julho de 2014): 386–91. http://dx.doi.org/10.4028/www.scientific.net/amr.981.386.
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£o Based on judging the change of pulse amplitude of phase detecting, a new method of time domain coherency for the radar emitter sorting is proposed in this paper. We analyzed the theory of the signal coherency recognition method. Then, the mathematic derivation process and specific recognition steps of the new method are detailedly given. But above all, the new method can be applied into the signal sorting of radar emitter. Simulation results also reveal that this new method can effectively sort the radar emitter signals under the low signal-to-noise ratio, and it is a huge help to solve the problem of " leakage-batch ". Results demonstrate that the new method has strong theoretical value.
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Wang, Jundi, Xing Wang, Yuanrong Tian, Zhenkun Chen e You Chen. "A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing". Electronics 11, n.º 7 (29 de março de 2022): 1078. http://dx.doi.org/10.3390/electronics11071078.
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Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly.
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Chen, Kuiyu, Shuning Zhang, Lingzhi Zhu, Si Chen e Huichang Zhao. "Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning". Sensors 21, n.º 2 (10 de janeiro de 2021): 449. http://dx.doi.org/10.3390/s21020449.
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Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.
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Chen, Kuiyu, Shuning Zhang, Lingzhi Zhu, Si Chen e Huichang Zhao. "Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning". Sensors 21, n.º 2 (10 de janeiro de 2021): 449. http://dx.doi.org/10.3390/s21020449.
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Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.
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Beskostyi, Dmitrii F., Sergei G. Borovikov, Yurii V. Yastrebov e Ilya A. Sozontov. "Use of Aposteriori Information in the Implementation of Radar Recognition Systems Using Neural Network Technologies". Journal of the Russian Universities. Radioelectronics 22, n.º 5 (4 de dezembro de 2019): 52–60. http://dx.doi.org/10.32603/1993-8985-2019-22-5-52-60.
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Introduction. The current need to obtain relevant, complete and reliable information about airborne objects has led to the continuous improvement of modern radar recognition systems (MRRS) as part of control systems. The development of modern MRRS has created objective prerequisites for the use of progressive and new methods and algorithms for the processing of signals using neural networks. The use of artificial neural networks with learning ability permits expansion to include many signs of recognition by using information obtained in the process of monitoring airspace.Aim. To formulate the problem and develop proposals for the use of posterior information for airspace control in radar recognition systems using neural network technologies.Materials and methods. Based on an analysis of the structure of a unified information network, an approach was formulated to facilitate the development of MRRS based on training technologies. Using the synthesis method, examples of technical solutions were proposed, which will allow the use of modern methods and signal processing algorithms using a posteriori information generated by the control system.Results. The study identified the principles of neural network training in solving the recognition problem in the process of functioning of radio electronic equipment (REE). The technical solutions pro-posed take the functioning of the integrated radar system into account, allowing the information parameters required for training MRRS in a single information field to be obtained. It is shown that the removal of restrictions associated with the functional autonomy of REE, allows the use of posterior information in the implementation of radar recognition systems. This also allows for an increase in the number of recognition signs used in the algorithms and for the database of portraits to be replenished. Conclusion. MRRS can be developed via training by removing the restrictions associated with the autonomous functioning of RES. This allows for the situational assessment to be enhanced and management decisions to be optimised.
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Berry, Paul, Ngoc Hung Nguyen e Hai-Tan Tran. "Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars". Sensors 20, n.º 3 (24 de janeiro de 2020): 665. http://dx.doi.org/10.3390/s20030665.
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The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined l 1 -norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.
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Austin, G. L., A. Bellon, M. Riley e E. Ballantyne. "Navigation by Computer Processing of Marine Radar Images". Journal of Navigation 38, n.º 3 (setembro de 1985): 375–83. http://dx.doi.org/10.1017/s0373463300032744.
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The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.
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Li, Ji, Huiqiang Zhang, Jianping Ou e Wei Wang. "A Radar Signal Recognition Approach via IIF-Net Deep Learning Models". Computational Intelligence and Neuroscience 2020 (28 de agosto de 2020): 1–8. http://dx.doi.org/10.1155/2020/8858588.
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In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.
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Xie, Peilong, Zhiqun Hu, Shujie Yuan, Jiafeng Zheng, Hanyuan Tian e Fen Xu. "RADAR Echo Recognition of Squall Line Based on Deep Learning". Remote Sensing 15, n.º 19 (27 de setembro de 2023): 4726. http://dx.doi.org/10.3390/rs15194726.
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Squall line (SL) is a convective weather process that often causes disasters. The automatic recognition and early warning of SL are important objectives in the field of meteorology. By collecting the new-generation weather RADARs (CINRAD/SA and CINRAD/SAD) base data during 12 SL weather events occurred in Jiangsu, Shanghai, Shandong, Hebei, and other regions of China from 2019 to 2021, the dataset has a total of 49,920 samples with a window size of 40 km. The 40 km area was labeled by employing manual classification and data augmentation to construct the deep learning dataset with a positive and negative sample ratio of 1:1, of which 80% and 20% are separated as the training and test set, respectively. Based on the echo height of each elevation beam at different distances, three deep learning-based models are trained for SL automatic recognition, which include a near-distance model (M1) trained by the data in nine RADAR elevation angles within 45 km from RADARs, a mid-distance model (M2) by the data in six elevations from 45 to 135 km, and a far-distance model (M3) by the data in three elevations from 135 to 230 km. A confusion matrix and its derived metrics including receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) are introduced as the indicators to evaluate the models by the test dataset. The results indicate that the accuracy of models are over 86% with the hit rates over 87%, the false alarm rates less than 21%, and the critical success indexes (CSI) surpass 78%. All the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.95, so the three models have high hit rates and low false alarm rates for ensuring SL discrimination. Finally, the effectiveness of the models is further demonstrated through two SL events detected with Nanjing, Yancheng and Qingpu RADARs.
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Dudczyk, J. "A method of feature selection in the aspect of specific identification of radar signals". Bulletin of the Polish Academy of Sciences Technical Sciences 65, n.º 1 (1 de fevereiro de 2017): 113–19. http://dx.doi.org/10.1515/bpasts-2017-0014.
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Abstract This article presents an important task of classification, i.e. mapping surfaces which separate patterns in feature space in the scope of radar emitter recognition (RER) and classification. Assigning a tested radar to a particular class is based on defining its location from the discriminating areas. In order to carry out the classification process, it is necessary to define metrics in the feature space as it is essential to estimate the distance of a classified radar from the centre of the class. The method presented in this article is based on extraction and selection of distinctive features, which can be received in the process of specific emitter identification (SEI) of radar signals, and on the minimum distance classification. The author suggests a RER system which consists of a few independent channels. The task of each channel is to calculate the distance of the tested radar from a given class and finally, set the correct identification coefficient for each recognized radar. Thus, a multichannel system with independent distance measurement is obtained, which makes it possible to recognize particular radar copies. This system is implemented in electronic intelligence (ELINT) system and tested in real battlefield conditions.
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Zhao, Lijun, Qingsheng Li e Bingbing Li. "SAR Target Recognition via Monogenic Signal and Gaussian Process Model". Mathematical Problems in Engineering 2022 (13 de setembro de 2022): 1–7. http://dx.doi.org/10.1155/2022/3086486.
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The monogenic signal and Gaussian process model are applied to synthetic aperture radar (SAR) target recognition. The monogenic signal is used to extract the features of the SAR image. The Gaussian process model is a statistical learning algorithm based on the Bayesian theory, which constructs a classification model by combining the kernel function and the probability judgement. Compared with the traditional classification model, the Gaussian process model can obtain higher classification efficiency and accuracy. During the implementation, the monogenic feature vector of the SAR image is used as the input, and the target label is used as the output to train the Gaussian process model. For the test sample to be classified, the target label is determined by calculating the posterior probability of each class using the Gaussian process model. In the experiments, the validations are carried out under typical conditions based on the MSTAR dataset. According to the experimental results, the proposed method maintains the highest performance under the standard operating condition, depression angle differences, and noise corruption, which verifies its effectiveness and robustness.
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Li, Huiqin, Yanling Li, Chuan He, Hui Zhang e Jianwei Zhan. "Radar Working State Recognition Based on the Unsupervised and Incremental Method". Journal of Sensors 2021 (7 de outubro de 2021): 1–14. http://dx.doi.org/10.1155/2021/8673046.
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Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm is used to cluster unlabeled samples to obtain the best initial clustering results. The clustering results are input as training samples into the BP neural network to train the recognition model, which realizes the unsupervised recognition. Secondly, the incremental AP (IAP) algorithm based on the K -nearest neighbor (KNN) idea is used to divide the incremental samples by calculating the closeness between samples. The incremental samples are added to the BP recognition model as a new known state to complete the model update, which realizes incremental recognition. The simulation experiments on three types of radar data sets show that the recognition accuracy of the proposed model can reach more than 83%, which verifies the feasibility and effectiveness of the method. In addition, compared with the AP algorithm and K -means algorithm, the improved AP method improves 59.4%, 17.6%, and 53.5% in purity, rand index (RI), and F -measure indexes, respectively, and the running time is at least 34.8% shorter than the AP algorithm. The time of processing incremental data is greatly reduced, and the clustering efficiency is improved. Experimental results show that this method can quickly and accurately identify radar working state and play an important role in giving full play to the adaptability and timeliness of the cognitive electronic countermeasures.
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HUANG, DE-SHUANG. "APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS". International Journal of Pattern Recognition and Artificial Intelligence 13, n.º 06 (setembro de 1999): 945–62. http://dx.doi.org/10.1142/s0218001499000525.
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This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.
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Li, Yinqi. "Using sensor fusion technology to realize pedestrian recognition and hazard assessment". Theoretical and Natural Science 28, n.º 1 (26 de dezembro de 2023): 30–35. http://dx.doi.org/10.54254/2753-8818/28/20230463.
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The prevailing technology for pedestrian recognition in unmanned driving, predominantly reliant on LiDAR, confronts the dual challenges of elevated expenses and limited anti-interference capabilities. To surmount these obstacles, this paper introduces an inventive fusion methodology that harmonizes inputs from visual cameras, 4D millimeter wave radar, and thermal imaging sensors. The advantages and promising development prospects of 4D millimeter wave radar over laser radar are comprehensively elucidated. By leveraging advanced signal processing algorithms, a robust mathematical model is formulated, facilitating the synthesis of information from a multitude of distinctive feature parameters. In tandem, an assessment of the hazard index is executed using the analytic hierarchy process, enriching vehicular safety and driving efficiency. This innovative approach strives to foster the progression of autonomous vehicle technology and expedite its commercial assimilation into the burgeoning autonomous driving market. By harnessing the synergistic capabilities of multiple sensor modalities, the proposed fusion technique not only addresses the existing limitations but also charts a transformative course towards a safer and more efficient autonomous driving landscape. Through the amalgamation of these cutting-edge technologies, this research aspires to carve a path for the accelerated evolution and widespread deployment of autonomous vehicles.
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Cui, Hao, Min Su, Jia Liu e Lili Liu. "Template Construction of Radar Target Recognition based on Maximum Information Profile". Journal of Physics: Conference Series 2284, n.º 1 (1 de junho de 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2284/1/012021.
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Abstract High resolution range profile (HRRP) has the advantages of low complexity in imaging and processing. These features make HRRP widely adopted in radar automatic target recognition technology. Template matching is a representative technique for radar target recognition using HRRP. To overcome the attitude sensitivity problem of HRRP data, it is necessary to subdivide the training database according to attitude domain, and construct corresponding templates for matching. However, templates from traditional methods usually cause the loss of details due to the smoothing process, with limited classification performance. This paper is motivated by proposing a template generation approach based on maximum information profile, which retains the detailed information in templates for accuracy optimization. The proposed method is verified by the recognition experiments of three types of aircraft targets. The experimental results indicate that the maximum information profile outperform the mean range profile contributed by the angle domain template.
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Xue, Jian, Lan Tang, Xinggan Zhang, Lin Jin, Ming Hao e Youlin Gui. "Feature Evaluation and Comparison in Radar Emitter Recognition Based on SAHP". Electronics 10, n.º 11 (27 de maio de 2021): 1274. http://dx.doi.org/10.3390/electronics10111274.
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In the field of radar emitter recognition, with the wide application of modern radar, the traditional recognition method based on typical five feature parameters cannot achieve satisfactory recognition results in a complex electromagnetic environment. Currently, many new feature extraction methods are presented, but few approaches have been applied for feature evaluation or performance comparison. To deal with this problem, a feature evaluation and selection method was proposed based on set pair analysis (SPA) theory and analytic hierarchy process (AHP). The main idea of this method is to use SPA theory to solve problems regarding the construction of the decision matrix based on AHP, as it relies heavily on expert’s subjective experience. The aim was to improve the objectivity of the evaluation. To check the effectiveness of the proposed method, six feature parameters were selected for a comprehensive performance evaluation. Then, the convolutional neural network (CNN) was introduced to validate the recognition capability based on the evaluation results. Simulation results demonstrated that the proposed method could achieve the feature analysis and evaluation more reasonably and objectively.
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Li, Min, Gongjian Zhou, Bin Zhao e Taifan Quan. "Sparse Representation Denoising for Radar High Resolution Range Profiling". International Journal of Antennas and Propagation 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/875895.
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Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.
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Park, Dong Hyun, Dong-Ho Seo, Jee-Hyeon Baek, Won-Jin Lee e Dong Eui Chang. "A Novel Batch Streaming Pipeline for Radar Emitter Classification". Applied Sciences 13, n.º 22 (16 de novembro de 2023): 12395. http://dx.doi.org/10.3390/app132212395.
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In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.
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Hu, Rongchun, Zhenming Peng e Juan Ma. "A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching". Electronics 8, n.º 11 (1 de novembro de 2019): 1252. http://dx.doi.org/10.3390/electronics8111252.
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Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.
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Zhou, Junhao, Chao Sun, Kyongseok Jang, Shangyi Yang e Youngok Kim. "Human Activity Recognition Based on Continuous-Wave Radar and Bidirectional Gate Recurrent Unit". Electronics 12, n.º 19 (27 de setembro de 2023): 4060. http://dx.doi.org/10.3390/electronics12194060.
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The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%.
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Ye, Linting, Shengchang Lan, Kang Zhang e Guiyuan Zhang. "EM-Sign: A Non-Contact Recognition Method Based on 24 GHz Doppler Radar for Continuous Signs and Dialogues". Electronics 9, n.º 10 (26 de setembro de 2020): 1577. http://dx.doi.org/10.3390/electronics9101577.
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We studied continuous sign language recognition using Doppler radar sensors. Four signs in Chinese sign language and American sign language were captured and extracted by complex empirical mode decomposition (CEMD) to obtain spectrograms. Image sharpening was used to enhance the micro-Doppler signatures of the signs. To classify the different signs, we utilized an improved Yolov3-tiny network by replacing the framework with ResNet and fine-tuned the network in advance. This method can remove the epentheses from the training process. Experimental results revealed that the proposed method can surpass the state-of-the-art sign language recognition methods in continuous sign recognition with a precision of 0.924, a recall of 0.993, an F1-measure of 0.957 and a mean average precision (mAP) of 0.99. In addition, dialogue recognition in three daily conversation scenarios was performed and evaluated. The average word error rate (WER) was 0.235, 10% lower than in of other works. Our work provides an alternative form of sign language recognition and a new approach to simplify the training process and achieve a better continuous sign language recognition effect.
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Waqar, Sahil, Muhammad Muaaz e Matthias Pätzold. "Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems". Applied Sciences 12, n.º 4 (10 de fevereiro de 2022): 1825. http://dx.doi.org/10.3390/app12041825.
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In this paper, we highlight and resolve the shortcomings of single-input single-output (SISO) millimeter wave (mm-Wave) radar systems for human activity recognition (HAR). A 2×2 distributed multiple-input multiple-output (MIMO) radar framework is presented to capture human activity signatures under realistic conditions in indoor environments. We propose to distribute the two pairs of collocated transmitter–receiver antennas in order to illuminate the indoor environment from different perspectives. For the proposed MIMO system, we measure the time-variant (TV) radial velocity distribution and TV mean radial velocity to observe the signatures of human activities. We deploy the Ancortek SDR-KIT 2400T2R4 mm-Wave radar in a SISO as well as a 2×2 distributed MIMO configuration. We corroborate the limitations of SISO configurations by recording real human activities in different directions. It is shown that, unlike the SISO radar configuration, the proposed MIMO configuration has the ability to obtain superior human activity signatures for all directions. To signify the importance of the proposed 2×2 MIMO radar system, we compared the performance of a SISO radar-based passive step counter with a distributed MIMO radar-based passive step counter. As the proposed 2×2 MIMO radar system is able to detect human activity in all directions, it fills a research gap of radio frequency (RF)-based HAR systems.
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Wang, Xing, Wen Hong, Yunqing Liu, Dongmei Hu e Ping Xin. "SAR Image Aircraft Target Recognition Based on Improved YOLOv5". Applied Sciences 13, n.º 10 (17 de maio de 2023): 6160. http://dx.doi.org/10.3390/app13106160.
Texto completo da fonteResumo:
Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.