Статті в журналах: "Radar Recognition"

1

ŚWIĘTOCHOWSKI, Norbert, and Jacek PIONTEK. "ARTILLERY RADAR RECOGNITION." Scientific Journal of the Military University of Land Forces 164, no. 2 (March 1, 2012): 5–18. http://dx.doi.org/10.5604/01.3001.0002.2771.

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The authors present artillery radar recognition combat effectiveness and the way of its implementation in operations. Artillery firefinder radar stations are designed to provide deep reconnaissance for artillery fires. They provide information on enemy missiles, artil-lery and mortar fire positions. The first part of the article lists and describes the technical determinants which influence the operations of artillery radar stations. The factors affecting radar capabilities are the probability of the object location, coverage area and accuracy. In the other part of the article, the optional way of artillery radar operations is shown, in particular radar positioning and concealment.

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2

Ahmed, Shahzad, Karam Dad Kallu, Sarfaraz Ahmed, and Sung Ho Cho. "Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review." Remote Sensing 13, no. 3 (February 2, 2021): 527. http://dx.doi.org/10.3390/rs13030527.

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Human–Computer Interfaces (HCI) deals with the study of interface between humans and computers. The use of radar and other RF sensors to develop HCI based on Hand Gesture Recognition (HGR) has gained increasing attention over the past decade. Today, devices have built-in radars for recognizing and categorizing hand movements. In this article, we present the first ever review related to HGR using radar sensors. We review the available techniques for multi-domain hand gestures data representation for different signal processing and deep-learning-based HGR algorithms. We classify the radars used for HGR as pulsed and continuous-wave radars, and both the hardware and the algorithmic details of each category is presented in detail. Quantitative and qualitative analysis of ongoing trends related to radar-based HCI, and available radar hardware and algorithms is also presented. At the end, developed devices and applications based on gesture-recognition through radar are discussed. Limitations, future aspects and research directions related to this field are also discussed.

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3

Lundn, Jarmo, and Visa Koivunen. "Automatic Radar Waveform Recognition." IEEE Journal of Selected Topics in Signal Processing 1, no. 1 (June 2007): 124–36. http://dx.doi.org/10.1109/jstsp.2007.897055.

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4

Wang, Bin, Shunan Wang, Dan Zeng, and Min Wang. "Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition." Electronics 11, no. 9 (April 26, 2022): 1383. http://dx.doi.org/10.3390/electronics11091383.

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The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.

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5

Wang, Bin, Shunan Wang, Dan Zeng, and Min Wang. "Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition." Electronics 11, no. 9 (April 26, 2022): 1383. http://dx.doi.org/10.3390/electronics11091383.

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The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.

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6

Wang, Bin, Shunan Wang, Dan Zeng, and Min Wang. "Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition." Electronics 11, no. 9 (April 26, 2022): 1383. http://dx.doi.org/10.3390/electronics11091383.

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Анотація:

The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.

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7

Du, Congju, and Bin Tang. "Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs." Sensors 19, no. 10 (May 21, 2019): 2344. http://dx.doi.org/10.3390/s19102344.

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Radar unconventional active jamming, including unconventional deceptive jamming and barrage jamming, poses a serious threat to wideband radars. This paper proposes an unconventional-active-jamming recognition method for wideband radar. In this method, the visibility algorithm of converting the radar time series into graphs, called visibility graphs, is first given. Then, the visibility graph of the linear-frequency-modulation (LFM) signal is proved to be a regular graph, and the rationality of extracting features on visibility graphs is theoretically explained. Therefore, four features on visibility graphs, average degree, average clustering coefficient, Newman assortativity coefficient, and normalized network-structure entropy, are extracted from visibility graphs. Finally, a random-forests (RF) classifier is chosen for unconventional-active-jamming recognition. Experiment results show that recognition probability was over 90% when the jamming-to-noise ratio (JNR) was above 0 dB.

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8

Mi, Shengnan, Xinzhuo Liu, and Zhiyu Qu. "Recognition of Radar Signal Modulation Based on Fractional Fourier Transform." International Journal of Signal Processing Systems 5, no. 2 (June 2017): 65–69. http://dx.doi.org/10.18178/ijsps.5.2.65-69.

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9

Bartsch, A., F. Fitzek, and R. H. Rasshofer. "Pedestrian recognition using automotive radar sensors." Advances in Radio Science 10 (September 18, 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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10

Huang, Jing, Bin Wu, Peng Li, Xiao Li, and Jie Wang. "Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network." Remote Sensing 14, no. 7 (March 31, 2022): 1681. http://dx.doi.org/10.3390/rs14071681.

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In recent years, deep learning has been widely used in radar emitter signal identification and has significantly increased recognition rates. However, with the emergence of new institutional radars and an increasingly complex electromagnetic environment, the collection of high-quality signals becomes difficult, leading to a result that the amount of some signal types we own are too few to converge a deep neural network. Moreover, in radar emitter signal identification, most existing networks ignore the signal recognition of unknown classes, which is of vital importance for radar emitter signal identification. To solve these two problems, an improved prototypical network (IPN) belonging to metric-based meta-learning is proposed. Firstly, a reparameterization VGG (RepVGG) net is used to replace the original structure that severely limits the model performance. Secondly, we added a feature adjustment operation to prevent some extreme or unimportant samples from affecting the prototypes. Thirdly, open-set recognition is realized by setting a threshold in the metric module.

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11

Vinogradova, N. S., and L. G. Dorosinsky. "Recognition of radar images generated by synthetic aperture radar systems." Ural Radio Engineering Journal 5, no. 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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12

Ye, Fei, Xin Wang, Xing Rong Gao, and Jun Luo. "A New Recognition Method of Radar Emitter Signal." Applied Mechanics and Materials 380-384 (August 2013): 3509–12. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3509.

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According to the problem that the existing radar signal recognition method cannot effectively identify the radar signal, a new recognition method based on kernel density estimation is proposed. First using kernel density estimation gets the probability density curve of radar emitter signal parameters, then storing the cures into database as the characteristics, in the end a radar emitter signal recognition algorithm based on template matching is proposed.

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13

Zhang, Haoyu, Lei Yu, Yushi Chen, and Yinsheng Wei. "Fast Complex-Valued CNN for Radar Jamming Signal Recognition." Remote Sensing 13, no. 15 (July 22, 2021): 2867. http://dx.doi.org/10.3390/rs13152867.

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Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.

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14

Matuszewski, Jan, and Dymitr Pietrow. "Specific Radar Recognition Based on Characteristics of Emitted Radio Waveforms Using Convolutional Neural Networks." Sensors 21, no. 24 (December 9, 2021): 8237. http://dx.doi.org/10.3390/s21248237.

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With the increasing complexity of the electromagnetic environment and continuous development of radar technology we can expect a large number of modern radars using agile waveforms to appear on the battlefield in the near future. Effectively identifying these radar signals in electronic warfare systems only by relying on traditional recognition models poses a serious challenge. In response to the above problem, this paper proposes a recognition method of emitted radar signals with agile waveforms based on the convolutional neural network (CNN). These signals are measured in the electronic recognition receivers and processed into digital data, after which they undergo recognition. The implementation of this system is presented in a simulation environment with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized and written in the emitter database. This article contains a description of the software’s components, learning subsystem and signal generator. The problem of teaching neural networks with the use of the graphics processing units and the way of choosing the learning coefficients are also outlined. The correctness of the CNN operation was tested using a simulation environment that verified the operation’s effectiveness in a noisy environment and in conditions where many radar signals that interfere with each other are present. The effectiveness results of the applied solutions and the possibilities of developing the method of learning and processing algorithms are presented by means of tables and appropriate figures. The experimental results demonstrate that the proposed method can effectively solve the problem of recognizing raw radar signals with agile time waveforms, and achieve correct probability of recognition at the level of 92–99%.

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15

Kurlovich, V. I., and S. R. Geister. "Radar Detection and Recognition of Objects." Telecommunications and Radio Engineering 54, no. 8-9 (2000): 161–68. http://dx.doi.org/10.1615/telecomradeng.v54.i8-9.170.

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16

YONG, Xiao-ju, Deng-fu ZHANG, and Shi-qiang WANG. "Automatic recognition of radar pulse modulation." Journal of Computer Applications 31, no. 6 (June 13, 2012): 1730–32. http://dx.doi.org/10.3724/sp.j.1087.2011.01730.

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17

Gao, Hao, and Xu Dong Zhang. "Automatic Radar Waveform Recognition Using SVM." Applied Mechanics and Materials 229-231 (November 2012): 2348–51. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.2348.

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In this paper, a new feature for radar waveform recognition based on the instantaneous frequency is proposed. It is especially utilized for discriminating phase coded signals from other signals. Maximum likelihood estimation (MLE), autocorrelation algorithm, and likelihood ratio test are exploited in the algorithm. In the classification system, support vector machine (SVM) offers an efficient approach to classify linear frequency modulation (LFM) signals, phase coded signals and single frequency signals. Simulation results indicate that the new feature vectors perform effectively over a large range of SNRs. Furthermore, the new classifier achieves a very robust performance that the correct rate is over 90% at SNR of 5 dB, and the ever-increasing rate has been over 97% since SNR of 10dB.

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18

Wang, Yanhua, Xuejie Bi, Wei Chen, Yang Li, Qiao Chen, and Teng Long. "Deep forest for radar HRRP recognition." Journal of Engineering 2019, no. 21 (November 1, 2019): 8018–21. http://dx.doi.org/10.1049/joe.2019.0723.

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19

Song, Jia, Yanhua Wang, Wei Chen, Yang Li, and Junfu Wang. "Radar HRRP recognition based on CNN." Journal of Engineering 2019, no. 21 (November 1, 2019): 7766–69. http://dx.doi.org/10.1049/joe.2019.0725.

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20

Liu, Xinming. "Radar Recognition Based on SIFT Descriptor." Journal of Information and Computational Science 12, no. 3 (February 10, 2015): 935–42. http://dx.doi.org/10.12733/jics20105379.

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21

Klemm, R. "Recognition of convoys with array radar." IET Radar, Sonar & Navigation 6, no. 3 (2012): 123. http://dx.doi.org/10.1049/iet-rsn.2011.0077.

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22

Pisane, Jonathan, Sylvain Azarian, Marc Lesturgie, and Jacques Verly. "Automatic Target Recognition for Passive Radar." IEEE Transactions on Aerospace and Electronic Systems 50, no. 1 (January 2014): 371–92. http://dx.doi.org/10.1109/taes.2013.120486.

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23

Kawalec, A., and R. Owczarek. "The method for radar signal recognition." Journal de Physique IV (Proceedings) 137 (November 2006): 123–27. http://dx.doi.org/10.1051/jp4:2006137025.

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24

Moruzzis, M., and N. Colin. "Radar target recognition by Fuzzy Logic." IEEE Aerospace and Electronic Systems Magazine 13, no. 7 (July 1998): 13–20. http://dx.doi.org/10.1109/62.690808.

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25

Borden, B. "Problems in airborne radar target recognition." Inverse Problems 10, no. 5 (October 1, 1994): 1009–22. http://dx.doi.org/10.1088/0266-5611/10/5/002.

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26

Mao, Chengchen, and Jing Liang. "HRRP recognition in radar sensor network." Ad Hoc Networks 58 (April 2017): 171–78. http://dx.doi.org/10.1016/j.adhoc.2016.09.001.

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27

Wu, Bin, Shibo Yuan, Peng Li, Zehuan Jing, Shao Huang, and Yaodong Zhao. "Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism." Sensors 20, no. 21 (November 7, 2020): 6350. http://dx.doi.org/10.3390/s20216350.

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As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

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28

Cluckie, I. D., R. J. Griffith, A. Lane, and K. A. Tilford. "Radar hydrometeorology using a vertically pointing radar." Hydrology and Earth System Sciences 4, no. 4 (December 31, 2000): 565–80. http://dx.doi.org/10.5194/hess-4-565-2000.

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Abstract. A Vertically Pointing Radar (VPR) has been commissioned and deployed at a number of sites in southern England, to investigate numerically spatial and temporal variations in the vertical reflectivity profile (Zvp); particularly those associated with the intersection by the radar beam of a melting layer – the bright band. Comparisons with data from other instrumentation, notably with the S-band research radar at Chilbolton, but also with disdrometer data and rainfall measurements from a number of sophisticated rain gauges, show that VPR scans of the atmosphere provide detailed and reliable quantitative measurements of the Zvp. Analysis of a three year archive of Zvp data for Manchester has shown a bright band to be present in over 80% of rainfall events, highlighting the extent of the problem of bright band errors in scanning weather radar data. The primary characteristics of the bright band such as the height and magnitude (in dBZ) of the top, bottom and peak are identified objectively from VPR Zvp data by an automatic bright band recognition algorithm. It is envisaged that this approach could form the basis of an objective, automatic real time correction procedure for scanning weather radars. Keywords: Vertically Pointing Radar, weather radar, hydrometeorology, bright-band, melting-layer, vertical radar reflectivity

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29

Chaudhary, Sushank, Lunchakorn Wuttisittikulkij, Muhammad Saadi, Abhishek Sharma, Sattam Al Otaibi, Jamel Nebhen, Demostenes Zegarra Rodriguez, et al. "Coherent detection-based photonic radar for autonomous vehicles under diverse weather conditions." PLOS ONE 16, no. 11 (November 15, 2021): e0259438. http://dx.doi.org/10.1371/journal.pone.0259438.

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Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time.

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30

Zhang, Xiangli, Jiazhen Zhang, Tianze Luo, Tianye Huang, Zuping Tang, Ying Chen, Jinsheng Li, and Dapeng Luo. "Radar Signal Intrapulse Modulation Recognition Based on a Denoising-Guided Disentangled Network." Remote Sensing 14, no. 5 (March 4, 2022): 1252. http://dx.doi.org/10.3390/rs14051252.

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Accurate recognition of radar modulation mode helps to better estimate radar echo parameters, thereby occupying an advantageous position in the radar electronic warfare (EW). However, under low signal-to-noise ratio environments, recent deep-learning-based radar signal recognition methods often perform poorly due to the unsuitable denoising preprocess. In this paper, a denoising-guided disentangled network based on an inception structure is proposed to simultaneously complete the denoising and recognition of radar signals in an end-to-end manner. The pure radar signal representation (PSR) is disentangled from the noise signal representation (NSR) through a feature disentangler and used to learn a radar signal modulation recognizer under low-SNR environments. Signal noise mutual information loss is proposed to enlarge the gap between the PSR and the NSR. Experimental results demonstrate that our method can obtain a recognition accuracy of 98.75% in the −8 dB SNR and 89.25% in the −10 dB environment of 12 modulation formats.

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31

Li, Guangming, Jingye Yan, and Ailan Lan. "An Improved Meteor Echo Recognition Algorithm for SuperDARN HF Radar." Electronics 10, no. 16 (August 16, 2021): 1971. http://dx.doi.org/10.3390/electronics10161971.

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The SuperDARN HF radars can be used for meteor observation and inversion of mid-upper atmosphere neutral wind using observed meteor echo Doppler velocities. Aiming at the problem that the extraction of meteor echo based on echo power, Doppler velocity and spectral width is rough and contains ionospheric echo, this paper optimizes the extraction algorithm of meteor echo. Based on the AgileDARN HF radar’s digital characteristics, the observation method of meteor echo was improved, and we designed a meteor observation mode without changing the hardware system: using a meteor observation with a 7.5 km range resolution and a 2 s integration time, we extracted the Doppler characteristics of different echo types at meteor echo ranges; according to these features, the extraction algorithm of meteor echo was optimized. By analyzing the measured data, the characteristics of diurnal variation, power distribution, Doppler velocity distribution and spectral width distribution of meteor echo extracted by the optimization algorithm were obtained. The meteor echo characteristics obtained by the improved algorithm are more consistent with the theoretical analysis; thus, the improved algorithm is better than the SuperDARN high frequency radar meteor echo extraction algorithm and has good performance. The meteor echo extraction algorithm presented in this paper can extract the meteor echo more accurately, so that the atmospheric neutral wind can be retrieved more accurately. At the same time, the proposed algorithm is not only applicable to AgileDARN HF radar meteor observation mode data, but also to AgileDARN and SuperDARN normal mode data, which is beneficial to expand the data application of SuperDARN radars.

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32

Song, Seungeon, Bongseok Kim, Sangdong Kim, and Jonghun Lee. "Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning." Sensors 21, no. 11 (June 7, 2021): 3937. http://dx.doi.org/10.3390/s21113937.

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Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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33

Li, Huiqin, Yanling Li, Xuemei Wang, Zhe Xu, and Xinli Yin. "Radar Working State Recognition Based on Improved HPSO-BP." International Journal of Antennas and Propagation 2021 (April 7, 2021): 1–13. http://dx.doi.org/10.1155/2021/5586851.

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In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

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34

Korneev, P. E. "THE PROCESSING OF THE ELLIPTICALLY POLARIZED SIGNAL IN RADAR STATIONS WITH DIGITAL SYNTHESIS OF THE ANTENNA APERTURE." Civil Aviation High TECHNOLOGIES 22, no. 1 (February 27, 2019): 83–92. http://dx.doi.org/10.26467/2079-0619-2019-22-1-83-92.

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The processing of the elliptically polarized reflected signal in the Earth remote sensing systems makes it possible to obtain additional advantages when solving problems of recognition of the observable objects on the ground and under the ground. Full polarization reception implemented in radar stations with digital synthesis of the antenna aperture when remote sensing of the Earth increases the information content of such radars (the radar image of the investigated surface is detailed, the contrast of objects in the field of view is improved, and various negative effects of the image are minimized). The paper considers the quadrature processing of the reflected elliptically polarized signal in radar stations with digital synthesis of the antenna aperture in the mode of lateral survey of the terrestrial (water) surface. The processing of the reflected signal using the methods of radio polarimetry opens new possibilities for such radars while solving problems of remote sensing of the surface and recognition of radar targets. In addition, radar stations with digital synthesis of the antenna aperture with processing of an elliptically polarized signal have a higher interference immunity compared to radars, where a linearly polarized signal is processed. In the article, mathematical modeling is performed in the part of demodulation of the in-phase and quadrature components of the trajectory signal when the geometric parameters of the polarization ellipse change. The obtained analytical expressions allow estimating the influence of the geometric parameters of the polarization ellipse on the trajectory signal being processed. It is analytically confirmed that the angle of ellipticity affects the energy characteristics, and the orientation angle of the polarization ellipse introduces an additional phase shift in the characteristics of the trajectory signal being processed. Not taking into account these nuances while designing digital units and systems of such radars can lead to the loss of all the benefits of processing an elliptically polarized signal. The paper presents a structural scheme of the polarization radar station with digital synthesis of the antenna aperture.

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35

Quan, Daying, Zeyu Tang, Xiaofeng Wang, Wenchao Zhai, and Chongxiao Qu. "LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion." Symmetry 14, no. 3 (March 14, 2022): 570. http://dx.doi.org/10.3390/sym14030570.

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The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB.

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36

Vetoshkin, A., A. Аrtikula, and D. Britov. "OVERVIEW OF MODERN APPROACHES TO SOLVING THE PROBLEM OF EXTENDED RADAR TARGETS RECOGNITION." Наукові праці Державного науково-дослідного інституту випробувань і сертифікації озброєння та військової техніки, no. 5 (December 22, 2020): 14–19. http://dx.doi.org/10.37701/dndivsovt.5.2020.02.

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The development of the theory and technology of radio location indicates the need to solve problems of radar recognition of targets by spatial parameters. This is due to the need to provide in promising radar stations (complexes) with a software overview of the required resolution to obtain three-dimensional images in all spatial coordinates. The task of radar targets recognition, which is to assign the observed objects to the appropriate classes and types, is of considerable and growing interest. Different classes (types) of targets make up a certain alphabet, the choice of which determines not only the effectiveness of the use of recognition, but also the difficulties that arise in its implementation. Currently, there are a large number of radar recognition algorithms. They differ in the stages of decision-making, the degree and nature of accounting for statistics of signs, obstacles and signals. Due to the fact that the secondary emission pattern of extended targets is multi-lobed, statistical algorithms are preferred. The information used for radar recognition is contained in the set of received radar signals. However, most often for target recognition certain measured target features are used, which are compared in accordance with the recognition algorithms with known (reference) features. The choice of recognition features is usually made heuristically. This set of parameters does not always allow providing the required quality of recognition. The synthesized algorithms work unstable or require unreasonably large computational costs due to a significant increase in the dimensionality of the feature space. Analysis of known radar recognition algorithms of extended targets shows that they were developed under significant constraints. At present, the tasks of point targets radar surveillance are most fully solved. The tasks of processing signals reflected from bodies of complex shape, given the difficulties of their formulation and solution are not fully explored. It is promising to consider a set of radar surveillance tasks and the criteria used in them as a multicriteria task, the solution of which is associated with vector optimization of the location system as a whole.

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37

Guo, Qiang, Xin Yu, and Guoqing Ruan. "LPI Radar Waveform Recognition Based on Deep Convolutional Neural Network Transfer Learning." Symmetry 11, no. 4 (April 15, 2019): 540. http://dx.doi.org/10.3390/sym11040540.

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Low Probability of Intercept (LPI) radar waveform recognition is not only an important branch of the electronic reconnaissance field, but also an important means to obtain non-cooperative radar information. To solve the problems of LPI radar waveform recognition rate, difficult feature extraction and large number of samples needed, an automatic classification and recognition system based on Choi-Williams distribution (CWD) and depth convolution neural network migration learning is proposed in this paper. First, the system performs CWD time-frequency transform on the LPI radar waveform to obtain a 2-D time-frequency image. Then the system preprocesses the original time-frequency image. In addition, then the system sends the pre-processed image to the pre-training model (Inception-v3 or ResNet-152) of the deep convolution network for feature extraction. Finally, the extracted features are sent to a Support Vector Machine (SVM) classifier to realize offline training and online recognition of radar waveforms. The simulation results show that the overall recognition rate of the eight LPI radar signals (LFM, BPSK, Costas, Frank, and T1–T4) of the ResNet-152-SVM system reaches 97.8%, and the overall recognition rate of the Inception-v3-SVM system reaches 96.2% when the SNR is −2 dB.

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38

Lee, Gawon, and 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, no. 4 (February 18, 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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39

Gorobets’, V., M. Golovko, S. Zotov, and L. Kovorotny. "Additional radar signature for waterborne object recognition." RADIOFIZIKA I ELEKTRONIKA 26, no. 4 (2021): 16–21. http://dx.doi.org/10.15407/rej2021.04.016.

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Subject and Purpose. The article is devoted to the radio recognition of moving waterborne objects (sea-going ships). The problem lies in the lack of radar signatures, which is especially true for coherent radar in continuous mode, implying that more signatures for the waterborne object recognition is highly needed. An additional signature can be gained just by means of a simple mathematical processing of target reflection signals. This is particularly important for radio recognition systems in current use because this will hardly complicate the system structure. Hence, it will not affect its cost either. Methods and Methodology. The method developed for the retrieval of an additional radar signature characteristic of waterborne objects moving across a rough sea surface is based on a simple mathematical processing of a signal reflected from the moving waterborne object and taken from the phase output of coherent radar. The method approbation is by the mathematical modeling of signals at the phase detector output in the event of three waterborne objects such that have identical scattering cross sections but different periods of the side and keel vibrations. Results. Based on the mathematical modeling results, it has been shown that each of the local scattering centers keeps the ratio of the linear speeds of side and keel vibrations approximately the same for the same object. But the employed ratio takes different values for different objects. Conclusion. Having a single standard coherent radar in continuous mode and guided by the developed methodology, one can gain an additional signature for the target recognition, which is a ratio of the linear speeds of side and keel vibrations of the target. The suggested methodology can be used for the radio recognition of waterborne objects moving across a rough sea surface.

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40

Li, Xinyu, Yuan He, and Xiaojun Jing. "A Survey of Deep Learning-Based Human Activity Recognition in Radar." Remote Sensing 11, no. 9 (May 6, 2019): 1068. http://dx.doi.org/10.3390/rs11091068.

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Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.

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41

Wang, Chen Xi, Xin Chen, Han Yu Zou, Song He, and Xiao Tang. "Automatic Target Recognition of Millimeter-Wave Radar Based on Deep Learning." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012031. http://dx.doi.org/10.1088/1742-6596/2031/1/012031.

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Abstract All-day, all-weather wide-area search discovery target capability makes radar become a key piece of equipment in many military and civilian fields, and plays an indispensable role in tasks such as identification and positioning. However, at the same time, the current radar equipment generally relies on complex signal processing systems, and designing artificial feature extraction algorithms based on prior knowledge is both difficult and time-consuming, and it is difficult to fine-grained identify the target. In recent years, deep learning has been widely used in the radar field with its strong adaptability and strong self-learning ability. The paper presents a radar Doppler image based on YOLO v3 algorithm. With Millimeter-Wave radar distance-doppler reflectance image as input and darknet-53 as the feature extraction network, the automatic detection of vehicle millimeter wave radar cars, bicycles, pedestrians and trucks was realized, and the average detection accuracy reached 84.3%, providing new ideas and technical support for the development of intelligent radar and radar target detection.

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42

Kadochnikov, A., A. Kazantsev, O. Mishukov, and S. Shigorev. "Pattern Radar Images Formation’s Like a Stochastic Differential Equations for Recognition of Space Objects." Proceedings of Telecommunication Universities 5, no. 4 (2019): 106–13. http://dx.doi.org/10.31854/1813-324x-2019-5-4-106-113.

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The resource problems of the traditional use of detailed radar images for reliable recognition of space objects are shown. The urgent task of forming a new type of model of radar images of space objects to determine the signs of their recognition is posed. Corresponding mathematical models of such images based on stochastic differential equations of elliptic type are presented. The adequacy of the developed models to the real radar images of a space object was assessed. It is established that for the description of radar images of space objects the most suitable is a modified model in the form of a mixed derivative of an elliptical model. To test the hypothesis about the possibility of using the radar image model when constructing descriptive recognition signs, an experiment was conducted to distinguish four different types of space objects. The experimental results showed the possibility of using a mixed derivative of the elliptical model to determine signs of recognition of space objects.

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43

Lu, Zhang, Xu, Lin, and Huo. "A Deep Learning-Based Satellite Target Recognition Method Using Radar Data." Sensors 19, no. 9 (April 29, 2019): 2008. http://dx.doi.org/10.3390/s19092008.

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A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.

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44

Tahbaz Tavakoli, Erfan. "Radar Signal Recognition by CWD Picture Features." Int'l J. of Communications, Network and System Sciences 05, no. 04 (2012): 238–42. http://dx.doi.org/10.4236/ijcns.2012.54031.

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45

Wei, Yimin, Huadong Meng, Yimin Liu, and Xiqin Wang. "Extended Target Recognition in Cognitive Radar Networks." Sensors 10, no. 11 (November 11, 2010): 10181–97. http://dx.doi.org/10.3390/s101110181.

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46

Feng, Bo, Bo Chen, and Hongwei Liu. "Radar HRRP target recognition with deep networks." Pattern Recognition 61 (January 2017): 379–93. http://dx.doi.org/10.1016/j.patcog.2016.08.012.

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47

Li, Xu-tao, Shou-yong Wang, and Lian-wen Jin. "Radar Clutter Recognition Using Alpha Stable Distribution." Journal of Electronics & Information Technology 30, no. 9 (April 7, 2011): 2042–45. http://dx.doi.org/10.3724/sp.j.1146.2007.00853.

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48

Kong, Seung-Hyun, Minjun Kim, Linh Manh Hoang, and Eunhui Kim. "Automatic LPI Radar Waveform Recognition Using CNN." IEEE Access 6 (2018): 4207–19. http://dx.doi.org/10.1109/access.2017.2788942.

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49

Zhou, Daiying, Xiaofeng Shen, and Yangyang Liu. "NONLINEAR SUBPROFILE SPACE FOR RADAR HRRP RECOGNITION." Progress In Electromagnetics Research Letters 33 (2012): 91–100. http://dx.doi.org/10.2528/pierl12052302.

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50

Lee, Joon-Ho, and Hyo-Tae Kim. "Radar target recognition using least squares estimate." Microwave and Optical Technology Letters 30, no. 6 (2001): 427–34. http://dx.doi.org/10.1002/mop.1335.

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