Journal articles: 'Micro-Doppler radar'

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Gong, Jiangkun, Jun Yan, Deren Li, and Deyong Kong. "Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times." Drones 6, no. 9 (September 19, 2022): 262. http://dx.doi.org/10.3390/drones6090262.

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Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones.

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Krasnov, Oleg A., and Alexander G. Yarovoy. "Radar micro-Doppler of wind turbines: simulation and analysis using rotating linear wire structures." International Journal of Microwave and Wireless Technologies 7, no. 3-4 (June 2015): 459–67. http://dx.doi.org/10.1017/s1759078715000641.

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A simple electromagnetic model of wind-turbine's main structural elements as the linear wired structures is developed to simulate the temporal patterns of observed radar return Doppler spectra (micro-Doppler). Using the model, the micro-Doppler for different combinations of the turbines rotation frequency, radar pulse repetition frequency, and duration of the Doppler measurement interval are analyzed. The model is validated using the PARSAX radar experimental data. The model ability to reproduce the observed Doppler spectra main features can be used for development of signal-processing algorithms to suppress the wind-turbines clutter in modern Doppler radars.

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Hassan, Shahid, Xiangrong Wang, Saima Ishtiaq, Nasim Ullah, Alsharef Mohammad, and Abdulfattah Noorwali. "Human Activity Classification Based on Dual Micro-Motion Signatures Using Interferometric Radar." Remote Sensing 15, no. 7 (March 24, 2023): 1752. http://dx.doi.org/10.3390/rs15071752.

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Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved.

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Wang, Zhihao, Ying Luo, Kaiming Li, Hang Yuan, and Qun Zhang. "Micro-Doppler Parameters Extraction of Precession Cone-Shaped Targets Based on Rotating Antenna." Remote Sensing 14, no. 11 (May 26, 2022): 2549. http://dx.doi.org/10.3390/rs14112549.

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Micro-Doppler is regarded as a unique signature of a target with micro-motions. The sophisticated recognition of the cone-shaped targets can be realized through the micro-Doppler effect. However, it is difficult to extract the micro-motion features perpendicular to the radar line of sight (LOS) effectively. In this paper, a micro-Doppler parameters extraction method of the cone-shaped targets is put forward based on the rotating antenna. First, a new radar configuration is proposed, in which an antenna rotates uniformly on a fixed circle, thus producing Doppler frequency shift. Second, the expression of the micro-Doppler frequency shift induced by the precession cone-shaped target is derived. Then, the micro-Doppler curves of point scatterers at the cone top and bottom are separated by the smoothness of the curves, and the empirical mode decomposition (EMD) method is utilized for the detection and estimation of the coning frequency. Finally, the micro-motion components perpendicular to the radar LOS are inverted by the peak of micro-Doppler frequency curve. Simulation results prove the effectiveness and robustness of the proposed method.

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Zang, Bo, Mingzhe Zhu, Xianda Zhou, Lu Zhong, and Zijiao Tian. "Application of S-Transform Random Consistency in Inverse Synthetic Aperture Imaging Laser Radar Imaging." Applied Sciences 9, no. 11 (June 5, 2019): 2313. http://dx.doi.org/10.3390/app9112313.

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Under the same principle, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length, as the characteristic of multi-resolution, S transform could reduce the influence of the micro-Doppler component and enhance the imaging effect. This paper presents a method for micro-Doppler feature extraction in Inverse Synthetic Aperture Imaging Laser Radar (ISAIL) imaging. It is accessible and comprehensive, applying Random Sample Consensus (RANSAC) for the separation and reconstruction of micro-Doppler and rigid body signals. Experiments show that the method can effectively remove the micro-Doppler information and obtain a clear target distance-instantaneous Doppler image.

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Singh, Ashish Kumar, and Yong-Hoon Kim. "Classification of Drones Using Edge-Enhanced Micro-Doppler Image Based on CNN." Traitement du Signal 38, no. 4 (August 31, 2021): 1033–39. http://dx.doi.org/10.18280/ts.380413.

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The development of advanced radar system for detection and classification of UAVs is an essential requirement for today’s societal security. Such intelligent system could able to analyze the received radar signal and extract relevant information by utilizing sophisticated algorithm. In this letter, the utilization of micro-Doppler signature (MDS) for classification of drones, using convolutional neural network (CNN) model has been presented. We have generated images of micro-Doppler signatures using W-band radar system and used it for classification purpose. In this work, phase stretch transform (PST) has been utilized for edge detection and enhancement of the micro-Doppler images, to generate the edge-enhanced micro-Doppler image (EMDI). The comparison based on classification performance of CNN with different input datasets shows that the EMDI based CNN model outperformed the micro-Doppler image (MDI) based model.

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Harsh, Archit. "Measuring Radar Signatures of a Simple Pendulum using Cantenna Radar." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 5 (April 13, 2016): 6785–95. http://dx.doi.org/10.24297/ijct.v15i5.1653.

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This paper gives a detailed analysis of the physics of simple pendulum and the equations governing the motion and velocity. The pendulum works in three modes: simple, damped and driving and driving only. The signatures are evaluated and simulated by the means of four different approaches: Euler method, Euler-Cromer method, 2nd order Runge-kutta method and built-in ODE-23 matlab solver. The simulation results are compared to the measured radar signatures using a CANTENNA RADAR originally developed by MIT. The radar was operated in Doppler mode and the micro-Doppler effects associated with pendulum is studied. This paper attempts to provide an in-depth background and analysis of how the pendulum works and the associated micro-Doppler study using RADAR.

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Ding, Yipeng, Chengxi Lei, Xuemei Xu, Kehui Sun, and Ling Wang. "Human Micro-Doppler Frequency Estimation Approach for Doppler Radar." IEEE Access 6 (2018): 6149–59. http://dx.doi.org/10.1109/access.2018.2793277.

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Molchanov, Pavlo, Ronny I. A. Harmanny, Jaco J. M. de Wit, Karen Egiazarian, and Jaakko Astola. "Classification of small UAVs and birds by micro-Doppler signatures." International Journal of Microwave and Wireless Technologies 6, no. 3-4 (March 19, 2014): 435–44. http://dx.doi.org/10.1017/s1759078714000282.

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The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.

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Cui, Rui, Ai Guo Sheng, Ji Fei Pan, Bing He, and Jing Zhu. "Research on Jamming Method of False Target Based on Micro-Motion Modulation." Applied Mechanics and Materials 556-562 (May 2014): 2707–10. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2707.

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Micro-Doppler is a unique feature of radar target, and has been applied to target recognition of ISAR widely, but it can also destroy the quality of the target image at the same time. So a novel jamming method of false target base on Micro-Doppler modulation is presented in the paper. The phase of captured radar transmitting signal is been modulated, which can generate false Micro-Doppler features. The micro-Doppler imaging model of the rotating target is analyzed, and the jamming model based on Micro-Motion modulation is given. Finally, the simulation of jamming experiment is carried out. The results of simulation prove the method is corrective and effective.

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Tahmoush, Dave, and Jerry Silvious. "Gait Variations in Human Micro-Doppler." International Journal of Electronics and Telecommunications 57, no. 1 (March 1, 2011): 23–28. http://dx.doi.org/10.2478/v10177-011-0003-1.

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Gait Variations in Human Micro-DopplerMeasurement of human gait variation is important for security applications such as the indication of unexpected loading due to concealed weapons. To observe humans safely, unobtrusively, and without privacy issues, radar provides one method to detect abnormal activity without using images. In this paper we focus on modeling the characteristics of human walking parameters in order to determine signature differences that are distinguishable and to determine the variability of normal walking to be compared to armed or loaded walking. We extract micro-Doppler from motion-captured human gait models and verify the models with radar measurements. We then vary the model to determine the extent of normal micro-Doppler variation in multiple dimensions of human gait. We also characterize the ability of radar to determine gender and suggest that alternative views to the frontal view may be more discriminative.

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He, Yuan, Pascal Aubry, Francois Le Chevalier, and Alexander Yarovoy. "Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar." International Journal of Microwave and Wireless Technologies 6, no. 3-4 (March 25, 2014): 423–34. http://dx.doi.org/10.1017/s1759078714000087.

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A new approach is proposed to extract the slow-time feature of human motion in high-resolution radars. The approach is based on the self-similarity matrix (SSM) of the radar signals. The Mutual Information is used as a measure of similarity. The SSMs of different radar signals (high-resolution range profile, micro-Doppler, and range-Doppler video sequence) are compared, and the angel-invariant property of the SSMs is demonstrated. The SSM for different activities (i.e. walking and running) is extracted from range-Doppler video sequence and analyzed. Finally, simulation result is validated by experimental data.

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Zhu, Mingzhe, Xianda Zhou, Bo Zang, Baisheng Yang, and Mengdao Xing. "Micro-Doppler Feature Extraction of Inverse Synthetic Aperture Imaging Laser Radar Using Singular-Spectrum Analysis." Sensors 18, no. 10 (October 1, 2018): 3303. http://dx.doi.org/10.3390/s18103303.

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Different from microwave radar, laser radar could be more sensitive to the micro-Doppler (m-D) effect due to its wave length. This limits the application of conventional methods, such as time–frequency based approach, since the processing needs a receiver with much higher sampling frequency than microwave radar. In this paper, a micro-Doppler feature extraction algorithm is proposed for the inverse synthetic aperture imaging laser radar (ISAIL). Singular-spectrum analysis (SSA) is employed for separation and reconstruction of the micro-Doppler and rigid body signal. Clear ISAIL image is obtained by minimum entropy criteria after echo signal decomposition. After theoretical derivation, the computation efficiency and ability of the proposed method is proved by the results of simulation and real data of An-26.

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Ostovan, Mahdi, Sadegh Samadi, and Alireza Kazemi. "Generation of Human Micro-Doppler Signature Based on Layer-Reduced Deep Convolutional Generative Adversarial Network." Computational Intelligence and Neuroscience 2022 (April 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/7365544.

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Human activity recognition (HAR) using radar micro-Doppler has attracted the attention of researchers in the last decade. Using radar for human activity recognition has been very practical because of its unique advantages. There are several classifiers for the recognition of these activities, all of which require a rich database to produce fine output. Due to the limitations of providing and building a large database, radar micro-Doppler databases are usually limited in number. In this paper, a new method for the generation of radar micro-Doppler of the human body based on the deep convolutional generating adversarial network (DCGAN) is proposed. To generate the database, the required input is also generated by converting the existing motion database to simulated model-based radar data. The simulation results show the success of this method, even on a small amount of data.

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Fogle, O. Ryan, and Brian D. Rigling. "Micro-Range/Micro-Doppler Decomposition of Human Radar Signatures." IEEE Transactions on Aerospace and Electronic Systems 48, no. 4 (October 2012): 3058–72. http://dx.doi.org/10.1109/taes.2012.6324677.

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Heading, Emma, Si Tran Nguyen, David Holdsworth, and Iain M. Reid. "Micro-Doppler Signature Analysis for Space Domain Awareness Using VHF Radar." Remote Sensing 16, no. 8 (April 12, 2024): 1354. http://dx.doi.org/10.3390/rs16081354.

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The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing.

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Gokaraju, Jnana Sai Abhishek Varma, Weon Keun Song, Min-Ho Ka, and Somyot Kaitwanidvilai. "Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks." International Journal of Advanced Robotic Systems 18, no. 3 (May 1, 2021): 172988142110105. http://dx.doi.org/10.1177/17298814211010569.

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The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

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Tahmoush, David, and Jerry Silvious. "Radar Measurement of Human Polarimetric Micro-Doppler." Journal of Electrical and Computer Engineering 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/804954.

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We use polarimetric micro-Doppler for the detection of arm motion, especially for the classification of whether someone has their arms swinging and is thus unloaded. The arm is often bent at the elbow, providing a surface somewhat similar to a dihedral. This is distinct from the more planar surfaces of the body which allows us to isolate the signals of the arm (and knee). The dihedral produces a double bounce that can be seen in polarimetric radar data by measuring the phase difference between HH and VV. This measurement can then be used to determine whether the subject is unloaded.

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Cai, Chengjie, Weixian Liu, Jeffrey Shiang Fu, and Yilong Lu. "Radar Micro-Doppler Signature Analysis with HHT." IEEE Transactions on Aerospace and Electronic Systems 46, no. 2 (April 2010): 929–38. http://dx.doi.org/10.1109/taes.2010.5461668.

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Muaaz, Muhammad, Sahil Waqar, and Matthias Pätzold. "Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing." Sensors 23, no. 13 (June 22, 2023): 5810. http://dx.doi.org/10.3390/s23135810.

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RF sensing offers an unobtrusive, user-friendly, and privacy-preserving method for detecting accidental falls and recognizing human activities. Contemporary RF-based HAR systems generally employ a single monostatic radar to recognize human activities. However, a single monostatic radar cannot detect the motion of a target, e.g., a moving person, orthogonal to the boresight axis of the radar. Owing to this inherent physical limitation, a single monostatic radar fails to efficiently recognize orientation-independent human activities. In this work, we present a complementary RF sensing approach that overcomes the limitation of existing single monostatic radar-based HAR systems to robustly recognize orientation-independent human activities and falls. Our approach used a distributed mmWave MIMO radar system that was set up as two separate monostatic radars placed orthogonal to each other in an indoor environment. These two radars illuminated the moving person from two different aspect angles and consequently produced two time-variant micro-Doppler signatures. We first computed the mean Doppler shifts (MDSs) from the micro-Doppler signatures and then extracted statistical and time- and frequency-domain features. We adopted feature-level fusion techniques to fuse the extracted features and a support vector machine to classify orientation-independent human activities. To evaluate our approach, we used an orientation-independent human activity dataset, which was collected from six volunteers. The dataset consisted of more than 1350 activity trials of five different activities that were performed in different orientations. The proposed complementary RF sensing approach achieved an overall classification accuracy ranging from 98.31 to 98.54%. It overcame the inherent limitations of a conventional single monostatic radar-based HAR and outperformed it by 6%.

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Yan, Jun, Huiping Hu, Jiangkun Gong, Deyong Kong, and Deren Li. "Exploring Radar Micro-Doppler Signatures for Recognition of Drone Types." Drones 7, no. 4 (April 21, 2023): 280. http://dx.doi.org/10.3390/drones7040280.

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In this study, we examine the use of micro-Doppler signals produced by different blades (i.e., puller and lifting blades) to aid in radar-based target recognition of small drones. We categorize small drones into three types based on their blade types: fixed-wing drones with only puller blades, multi-rotor drones with only lifting blades, and hybrid vertical take-off and landing (VTOL) fixed-wing drones with both lifting and puller blades. We quantify the radar signatures of the three drones using statistical measures, such as signal-to-noise ratio (SNR), signal-to-clutter ratio (SCR), Doppler speed, Doppler frequency difference (DFD), and Doppler magnitude ratio (DMR). Our findings show that the micro-Doppler signals of lifting blades in all three drone types were stronger than those of puller blades. Specifically, the DFD and DMR values of pusher blades were below 100 Hz and 0.3, respectively, which were much smaller than the 200 Hz and 0.8 values for lifting blades. The micro-Doppler signals of the puller blades were weaker and more stable than those of the lifting blades. Our study demonstrates the potential of using micro-Doppler signatures modulated by different blades for improving drone detection and the identification of drone types by drone detection radar.

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Garcia-Benadí, Albert, Joan Bech, Sergi Gonzalez, Mireia Udina, and Bernat Codina. "A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations." Remote Sensing 13, no. 21 (October 27, 2021): 4323. http://dx.doi.org/10.3390/rs13214323.

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The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-Pro (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-Pro manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-Pro which is freely available in the GitHub repository.

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Harmanny, Ronny I. A., Jacco J. M. de Wit, and Gilles Premel-Cabic. "Radar micro-Doppler mini-UAV classification using spectrograms and cepstrograms." International Journal of Microwave and Wireless Technologies 7, no. 3-4 (June 2015): 469–77. http://dx.doi.org/10.1017/s1759078715001002.

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The radar micro-Doppler signature of a target is determined by parts of the target moving or rotating in addition to the main body motion. The relative motion of these parts is characteristic for different classes of targets, e.g. the flapping motion of a bird's wings versus the spinning of propeller blades. In the present study, the micro-Doppler signature is exploited to discriminate birds and small unmanned aerial vehicles (UAVs). Emphasis is on micro-Doppler features that can be extracted from spectrograms and cepstrograms, enabling the human eye or indeed automatic classification algorithms to make a quick distinction between man-made objects and bio-life. In addition, in case of man-made objects, it is desired to further characterize the type of mini-UAV to aid the threat assessment. Also this characterization is done on the basis of micro-Doppler features.

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Qin, Xiaoyu, Bin Deng, and Hongqiang Wang. "Micro-Doppler Feature Extraction of Rotating Structures of Aircraft Targets with Terahertz Radar." Remote Sensing 14, no. 16 (August 9, 2022): 3856. http://dx.doi.org/10.3390/rs14163856.

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The micro-Doppler features formed by the micro-motion of rotating blades of rotors and turbines are of great significance for aircraft target detection and recognition. Mastering the micro-motion features is the premise of radar target identification. The blades’ length and rotation rate are vital parameters for classifying aircraft targets. One can instantly judge the type and state of the targets by extracting micro-Doppler features. To extract the micro-Doppler features of rotating blades of the turbine target, we utilized microwave-band and terahertz-band radar to simulate the target and extract the Doppler frequency-shift information. For a turbine model with an obvious blade tip structure, we propose an algorithm based on wavelet coefficient enhancement and inverse Radon transform, integrating the time–frequency analysis with image processing. Under low SNR, this method allows for a high-accuracy parameter estimate. For a two-bladed rotor model without an obvious blade tip structure, we conducted an actual measurement experiment on the model utilizing a 120 GHz radar, and we propose a parameter estimation algorithm based on the fitting of the time–frequency distribution. By fitting the data of the time–frequency diagram, the micro-motion characteristic parameters of the rotor target were obtained. The simulation and experimental results demonstrate the benefits of terahertz radar in target detection, and indicate that the proposed algorithms have the characteristics of high extraction precision and insensitivity to noise.

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Zhuang, Jing-bo, Zhen-miao Deng, Yi-shan Ye, Yi-xiong Zhang, and Yan-yong Chen. "Micro-Doppler Ambiguity Resolution Based on Short-Time Compressed Sensing." Journal of Electrical and Computer Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/864508.

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When using a long range radar (LRR) to track a target with micromotion, the micro-Doppler embodied in the radar echoes may suffer from ambiguity problem. In this paper, we propose a novel method based on compressed sensing (CS) to solve micro-Doppler ambiguity. According to the RIP requirement, a sparse probing pulse train with its transmitting time random is designed. After matched filtering, the slow-time echo signals of the micromotion target can be viewed as randomly sparse sampling of Doppler spectrum. Select several successive pulses to form a short-time window and the CS sensing matrix can be built according to the time stamps of these pulses. Then performing Orthogonal Matching Pursuit (OMP), the unambiguous micro-Doppler spectrum can be obtained. The proposed algorithm is verified using the echo signals generated according to the theoretical model and the signals with micro-Doppler signature produced using the commercial electromagnetic simulation software FEKO.

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Zhang, Yongqiang, Xiaopeng Li, Guilei Ma, Jinlong Ma, Menghua Man, and Shanghe Liu. "A New Model for Human Running Micro-Doppler FMCW Radar Features." Applied Sciences 13, no. 12 (June 15, 2023): 7190. http://dx.doi.org/10.3390/app13127190.

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Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model.

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Apriono, Catur, Fathul Muin, and Filbert H. Juwono. "Portable Micro-Doppler Radar with Quadrature Radar Architecture for Non-Contact Human Breath Detection." Sensors 21, no. 17 (August 28, 2021): 5807. http://dx.doi.org/10.3390/s21175807.

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Recently, rapid advances in radio detection and ranging (radar) technology applications have been implemented in various fields. In particular, micro-Doppler radar has been widely developed to perform certain tasks, such as detection of buried victims in natural disaster, drone system detection, and classification of humans and animals. Further, micro-Doppler radar can also be implemented in medical applications for remote monitoring and examination. This paper proposes a human respiration rate detection system using micro-Doppler radar with quadrature architecture in the industrial, scientific, and medical (ISM) frequency of 5.8 GHz. We use a mathematical model of human breathing to further explore any insights into signal processes in the radar. The experimental system is designed using the USRP B200 mini-module as the main component of the radar and the Vivaldi antennas working at 5.8 GHz. The radar system is integrated directly with the GNU Radio Companion software as the processing part. Using a frequency of 5.8 GHz and USRP output power of 0.33 mW, our proposed method was able to detect the respiration rate at a distance of 2 m or less with acceptable error. In addition, the radar system could differentiate different frequency rates for different targets, demonstrating that it is highly sensitive. We also emphasize that the designed radar system can be used as a portable device which offers flexibility to be used anytime and anywhere.

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Wang, Zhihao, Yijun Chen, Hang Yuan, Ying Luo, and Qun Zhang. "Real Micro-Doppler Parameters Extraction of Spinning Targets Based on Rotating Interference Antenna." Remote Sensing 14, no. 21 (October 23, 2022): 5300. http://dx.doi.org/10.3390/rs14215300.

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Micro-Doppler is a unique characteristic of targets with micro-motions, which can provide significant information for target classification and recognition. However, the monostatic radar has the shortcoming of only obtaining the radial micro-motion characteristics. Although the vortex-electromagnetic-wave-based radar has the potential to obtain real micro-motion parameters, it has a high dependence on the mode number and purity of the orbital angular momentum, which greatly restricts its application in the micro-motion parameter extraction. To overcome the above problems, a new radar configuration based on the rotating interference antenna is proposed in this paper. Through the interference processing of the micro-Doppler curves of the rotating and fixed antenna, the curves containing the real micro-motion information of the target can be obtained. Then the real micro-motion characteristics of the spinning target can be reconstructed by the orthogonal matching pursuit algorithm. The effectiveness and robustness of the proposed method are validated by simulations.

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Hong, Yonggi, Yunji Yang, and Jaehyun Park. "Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul." Sensors 21, no. 9 (April 21, 2021): 2924. http://dx.doi.org/10.3390/s21092924.

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In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

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Ferrone, Alfonso, Anne-Claire Billault-Roux, and Alexis Berne. "ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO)." Atmospheric Measurement Techniques 15, no. 11 (June 14, 2022): 3569–92. http://dx.doi.org/10.5194/amt-15-3569-2022.

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Abstract. The Micro Rain Radar PRO (MRR-PRO) is a K-band Doppler weather radar, using frequency-modulated continuous-wave (FMCW) signals, developed by Metek Meteorologische Messtechnik GmbH (Metek) as a successor to the MRR-2. Benefiting from four datasets collected during two field campaigns in Antarctica and Switzerland, we developed a processing library for snowfall measurements named ERUO (Enhancement and Reconstruction of the spectrUm for the MRR-PRO), with a twofold objective. Firstly, the proposed method addresses a series of issues plaguing the radar variables, including interference lines and power drops at the extremes of the Doppler spectrum. Secondly, the algorithm aims to improve the quality of the final variables by lowering the minimum detectable equivalent attenuated reflectivity factor and extending the valid Doppler velocity range through dealiasing. The performance of the algorithm has been tested against the measurements of a co-located W-band Doppler radar. Information from a close-by X-band Doppler dual-polarization radar has been used to exclude unsuitable radar volumes from the comparison. Particular attention has been dedicated to verifying the estimation of the meteorological signal in the spectra covered by interferences.

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Narayanan, Ram M., and Matthew Zenaldin. "Radar micro‐Doppler signatures of various human activities." IET Radar, Sonar & Navigation 9, no. 9 (December 2015): 1205–15. http://dx.doi.org/10.1049/iet-rsn.2015.0173.

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Li Yuexin, 李跃新, 孙建锋 Sun Jianfeng, 周煜 Zhou Yu, 卢智勇 Lu Zhiyong, 蔡新雨 Cai Xinyu, and 从海胜 Cong Haisheng. "Interference Suppression of Coherent Laser Micro Doppler Radar." Chinese Journal of Lasers 47, no. 10 (2020): 1010001. http://dx.doi.org/10.3788/cjl202047.1010001.

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Smith, G. E., K. Woodbridge, C. J. Baker, and H. Griffiths. "Multistatic micro-Doppler radar signatures of personnel targets." IET Signal Processing 4, no. 3 (2010): 224. http://dx.doi.org/10.1049/iet-spr.2009.0058.

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Stankovic, Ljubisa, Thayananthan Thayaparan, Milos Dakovic, and Vesna Popovic-Bugarin. "Micro-Doppler Removal in the Radar Imaging Analysis." IEEE Transactions on Aerospace and Electronic Systems 49, no. 2 (April 2013): 1234–50. http://dx.doi.org/10.1109/taes.2013.6494410.

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Ritchie, M., R. Capraru, and F. Fioranelli. "Dop‐NET: a micro‐Doppler radar data challenge." Electronics Letters 56, no. 11 (May 2020): 568–70. http://dx.doi.org/10.1049/el.2019.4153.

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Zhang, Zhaonian, Philippe O. Pouliquen, Allen Waxman, and Andreas G. Andreou. "Acoustic micro-Doppler radar for human gait imaging." Journal of the Acoustical Society of America 121, no. 3 (March 2007): EL110—EL113. http://dx.doi.org/10.1121/1.2437842.

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Dong, Wei-guang, and Yan-jun Li. "Radar target recognition based on micro-Doppler effect." Optoelectronics Letters 4, no. 6 (November 2008): 456–59. http://dx.doi.org/10.1007/s11801-008-8083-4.

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Li, Wenchao, Gangyao Kuang, and Boli Xiong. "Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD." Applied Sciences 8, no. 10 (October 2, 2018): 1801. http://dx.doi.org/10.3390/app8101801.

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Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Doppler signals based on Hilbert–Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.

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Klaer, Peter, Andi Huang, Pascale Sévigny, Sreeraman Rajan, Shashank Pant, Prakash Patnaik, and Bhashyam Balaji. "An Investigation of Rotary Drone HERM Line Spectrum under Manoeuvering Conditions." Sensors 20, no. 20 (October 21, 2020): 5940. http://dx.doi.org/10.3390/s20205940.

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Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

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Passafiume, Marco, Neda Rojhani, Giovanni Collodi, and Alessandro Cidronali. "Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar." Electronics 10, no. 6 (March 22, 2021): 747. http://dx.doi.org/10.3390/electronics10060747.

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With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the most distinctive information to identify and classify targets in many radar systems. In this paper, we introduce an effective model for the micro-Doppler effect that is suitable for frequency-modulated continuous-wave (FMCW) radar applications, and exploit it to investigate UAV signatures. The latter depends on the number of UAV motors, which are considered vibrational sources, and their rotation speed. To demonstrate the reliability of the proposed model, it is used to build simulated FMCW radar images, which are compared with experimental data acquired by a 77 GHz FMCW multiple-input multiple-output (MIMO) cost-effective automotive radar platform. The experimental results confirm the model’s ability to estimate the class of the UAV, namely its number of motors, in different operative scenarios. In addition, the experimental results show that the motors rotation speed does not imprint a significant signature on the classification of the UAV; thus, the estimation of the number of motors represents the only viable parameter for small UAV classification using the micro-Doppler effect.

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Guanghu, Jin, Dong Zhen, Yongsheng Zhang, and Feng He. "Template free Micro Doppler Signature Classification for Wheeled and Tracked Vehicles." Defence Science Journal 69, no. 5 (September 17, 2019): 517–27. http://dx.doi.org/10.14429/dsj.69.12096.

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The micro-Doppler signature is a time-varying frequency modulation imparted on radar echo caused by target’s micro-motion. To save the trouble of constructing template in the target classification, this paper investigates the micro-Doppler signature of wheeled and tracked vehicles and proposes a template-free classification method. Firstly, the echo signature is established and the micro-Doppler difference of these two kinds of targets is analysed. Secondly, some new micro-Doppler features are defined according to their difference. The new defined features are micro-Doppler bandwidth, micro-Doppler expansion rate and micro-Doppler peak number. According to the characteristic of the micro-Doppler in the time-frequency domain, we proposed to realise the feature extraction by Hough transformation. Lastly, template-free subjection functions are proposed to define the relationship between the features and the vehicles. By fuzzy comprehensive evaluation, the final classification result is obtained by combining the subjection probabilities together. Experimental results based on the simulated data and measured data are presented, which prove that the algorithm has good performance.

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Bu, Lijun, Yongzhong Zhu, Yijun Chen, Xiaoou Song, Yufei Yang, and Yadan Zang. "Micro-Motion Parameter Extraction of Multi-Scattering-Point Target Based on Vortex Electromagnetic Wave Radar." Remote Sensing 14, no. 23 (November 22, 2022): 5908. http://dx.doi.org/10.3390/rs14235908.

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In addition to traditional linear Doppler shift, the angular Doppler shift in vortex electromagnetic wave (VEMW) radar systems carrying orbital angular momentum (OAM) can provide more accurate target identification micro-motion parameters, especially the detailed features perpendicular to the radar line-of-sight (LOS) direction. In this paper, a micro-motion feature extraction method for a spinning target with multiple scattering points based on VEMW radar is proposed. First, a multi-scattering-point spinning target detection model using vortex radar is established, and the mathematical mechanism of echo signal flash shift in time-frequency (TF) domain is deduced. Then, linear Doppler shift is eliminated by interference processing with opposite dual-mode VEMW. Subsequently, the shift in TF flicker is focused on the reference zero frequency by the iterative phase compensation method, and the number of scattering points is estimated according to the focusing effect. After this, through the constructed compensation phase, the angular Doppler shift is separated, then the angular velocity, rotation radiusand initial phase of the target are estimated. Theoretical and simulation results verify the effectiveness of the proposed method, and more accurate rotation parameters can be obtained in the case of multiple scattering points using the VEMW radar system.

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Maahn, M., and P. Kollias. "Improved Micro Rain Radar snow measurements using Doppler spectra post-processing." Atmospheric Measurement Techniques Discussions 5, no. 4 (July 12, 2012): 4771–808. http://dx.doi.org/10.5194/amtd-5-4771-2012.

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Abstract. The Micro Rain Radar (MRR) is a compact Frequency Modulated Continuous Wave (FMCW) system that operates at 24 GHz. The MRR is a low-cost, portable radar system that requires minimum supervision in the field. As such, the MRR is a frequently used radar system for conducting precipitation research. Current MRR drawbacks are the lack of a sophisticated post-processing algorithm to improve its sensitivity (currently at +3 dBz), spurious artefacts concerning radar receiver noise and the lack of high quality Doppler radar moments. Here we propose an improved processing method which is especially suited for snow observations and provides reliable values of effective reflectivity, Doppler velocity and spectral width. The proposed method is freely available on the web and features a noise removal based on recognition of the most significant peak. A dynamic dealiasing routine allows observations even if the Nyquist velocity range is exceeded. Collocated observations at 115 days of a MRR and a pulsed 35.2 GHz MIRA35 cloud radar show a very high agreement for the proposed method for snow, if reflectivities are larger than −5 dBz. The overall sensitivity is increased to −14 and −8 dBz, depending on range. The proposed method exploits the full potential of MRR's hardware and substantially enhances the use of Micro Rain Radar for studies of solid precipitation.

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Maahn, M., and P. Kollias. "Improved Micro Rain Radar snow measurements using Doppler spectra post-processing." Atmospheric Measurement Techniques 5, no. 11 (November 12, 2012): 2661–73. http://dx.doi.org/10.5194/amt-5-2661-2012.

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Abstract. The Micro Rain Radar 2 (MRR) is a compact Frequency Modulated Continuous Wave (FMCW) system that operates at 24 GHz. The MRR is a low-cost, portable radar system that requires minimum supervision in the field. As such, the MRR is a frequently used radar system for conducting precipitation research. Current MRR drawbacks are the lack of a sophisticated post-processing algorithm to improve its sensitivity (currently at +3 dBz), spurious artefacts concerning radar receiver noise and the lack of high quality Doppler radar moments. Here we propose an improved processing method which is especially suited for snow observations and provides reliable values of effective reflectivity, Doppler velocity and spectral width. The proposed method is freely available on the web and features a noise removal based on recognition of the most significant peak. A dynamic dealiasing routine allows observations even if the Nyquist velocity range is exceeded. Collocated observations over 115 days of a MRR and a pulsed 35.2 GHz MIRA35 cloud radar show a very high agreement for the proposed method for snow, if reflectivities are larger than −5 dBz. The overall sensitivity is increased to −14 and −8 dBz, depending on range. The proposed method exploits the full potential of MRR's hardware and substantially enhances the use of Micro Rain Radar for studies of solid precipitation.

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Kim, Ji-Hyeon, Soon-Young Kwon, and Hyoung-Nam Kim. "Spectral-Kurtosis and Image-Embedding Approach for Target Classification in Micro-Doppler Signatures." Electronics 13, no. 2 (January 16, 2024): 376. http://dx.doi.org/10.3390/electronics13020376.

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Micro-Doppler signature represents the micromotion state of a target, and it is used in target recognition and classification technology. The micro-Doppler frequency appears as a transition of the Doppler frequency due to the rotation and vibration of an object. Thus, tracking and classifying targets with high recognition accuracy is possible. However, it is difficult to distinguish the types of targets when subdividing targets with the same micromotion or classifying different targets with similar velocities. In this study, we address the problem of classification of three different targets with similar speeds and segmentation of the same type of targets. A novel signature extraction procedure is developed to automatically recognize drone, bird, and human targets by exploiting the different micro-Doppler signatures exhibited by each target. The developed algorithm is based on a novel adaptation of the spectral kurtosis technique of the radar echoes reflected by the three target types. Further, image-embedding layers are used to classify the spectral kurtosis of objects with the same micromotion. We apply a ResNet34 deep neural network to micro-Doppler images to analyze its performance in classifying objects performing micro-movements on the collected bistatic radar data. The results demonstrate that the proposed method accurately differentiates the three targets and effectively classifies multiple targets with the same micromotion.

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Leonardi, Mauro, Gianluca Ligresti, and Emilio Piracci. "Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis." Drones 6, no. 5 (May 11, 2022): 124. http://dx.doi.org/10.3390/drones6050124.

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The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a time–frequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the drone’s micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present.

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Lu, Liangyou, Peng Chen, and Lenan Wu. "A RPCA-Based ISAR Imaging Method for Micromotion Targets." Sensors 20, no. 10 (May 25, 2020): 2989. http://dx.doi.org/10.3390/s20102989.

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Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed. We formulate the model of sparse ISAR imaging for micromotion target in the framework of RPCA. Then, the imaging problem is decomposed into iterations between the sub-problem of sparse imaging and Micro-Doppler extraction. The alternative direction method of multipliers (ADMM) approach is utilized to seek for the solution of each sub-problem. Furthermore, to improve the computational efficiency and numerical robustness in the Micro-Doppler extraction, an SVD-free method is presented to further lessen the calculative burden. Experimental results with simulated data validate the effectiveness of the proposed method.

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Siti Aminah Zulkarnain, Safiah Zulkifli, and ‘Aiffah Mohd Ali. "Identification and Analysis of Micro-Doppler Signature of a Bird Versus Micro-UAV." Journal of Advanced Research in Micro and Nano Engieering 16, no. 1 (March 22, 2024): 102–13. http://dx.doi.org/10.37934/armne.16.1.102113.

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The groundbreaking advancement of micro unmanned aerial vehicles (micro-UAVs) has been staggering. The diversity of micro-UAV operations is demanding in most sectors. However, the current regulatory framework for the civilian use of these devices is still insufficient. The operation of micro-UAVs may pose risks, including privacy violations and collision hazards. To address these concerns, a radar with advanced processing is needed. This study presents a preliminary design of an S-band continuous wave (CW) radar, which was simulated using MATLAB. The size of the rotating propeller blades of the micro-UAV ranges from 20 to 40 cm in length, while the size of a bird’s flapping wing measures 35 cm in length, comprising 22 cm for the upper arm and 13 cm for the lower arm. The analysis was conducted under hovering conditions, where the target's main body is stationary while its micro-parts move continuously. The Short-Time Fourier Transform (STFT) analysis successfully identified the unique signature of both targets. The results showed that the S-band CW radar design at 5 GHz is effective in extracting the micro-Doppler signature of a bird versus a micro-UAV. The extracted features can be used as additional characteristics for target classification in the future.

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Luo, Ying, Yi‐Jun Chen, Yong‐Zhong Zhu, Wang‐Yang Li, and Qun Zhang. "Doppler effect and micro‐Doppler effect of vortex‐electromagnetic‐wave‐based radar." IET Radar, Sonar & Navigation 14, no. 1 (January 2020): 2–9. http://dx.doi.org/10.1049/iet-rsn.2019.0124.

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Lei, Chengxi, and Yipeng Ding. "Legs Components Micro-Doppler Estimation of Human Target Based on Doppler Radar." Journal of Physics: Conference Series 1544 (May 2020): 012044. http://dx.doi.org/10.1088/1742-6596/1544/1/012044.

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Journal articles on the topic 'Micro-Doppler radar'

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