Multi-Person Recognition Using Separated Micro-Doppler Signatures

Huang, Xuejun; Ding, Jian; Liang, Dongxing; Wen, Liwu

doi:10.1109/jsen.2020.2977170

Cited by 28 publications

(12 citation statements)

References 16 publications

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“…The raw Range-Doppler images were directly fed into a CNN, resulting in performance with 99% accuracy for distinguishing humans from robots. Many other applications that use neural networks for radar problems have been tackled in the literature [ 38 , 39 , 40 , 41 , 42 ].…”

Section: State Of the Artmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Mohanna

Gianoglio

Rizik

et al. 2022

Sensors

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The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.

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Section: State Of the Artmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

Mohanna

Gianoglio

Rizik

et al. 2022

Sensors

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“…In the same reference [ 79 ], a brief review of the key developments in radar and signal processing techniques applied to the estimation of significant target parameters, such as range, velocity, and direction, are presented with mathematical illustrations. Thanks to extensive use in the automotive industry for various applications and due to advances in signal processing by applying machine learning, pattern recognition techniques, and robust algorithm developments [ 80 ], radar data now have more knowledge of object dimension [ 81 ], object orientation, motion prediction [ 82 ], and classification information [ 83 , 84 , 85 ].…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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“…Du et al [13] developed a range-velocity-time 3-D model that depicts the motions of different people in a multi-target situation by combining the micro-Doppler signature with range information. In [14], a separation method was proposed to split the individual micro-Doppler components from the multi-target micro-Doppler signatures. The separated components were then trained and tested using a separation convolutional neural network with a residual dense network.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Classification with Adaptive Thresholding using Radar Micro-Doppler

Kernec

Fioranelli

et al. 2021

2021 CIE International Conference on Radar (Radar)

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Radar systems are increasingly being used for healthcare applications for human activity recognition due to their advantages for privacy compliance, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are often very complex, hence requiring significant computational resources. We propose an adaptive thresholding algorithm used as a 'mask' to highlight the region of interest from the micro-Doppler signature. The mask is then applied to spectrogram information. These masked signatures are used for handcrafted feature extraction and classification. A quadratic-SVM classifier is employed based on the features from the information acquired. The preliminary results show that an accuracy of 91.3% is achieved using sequential forward feature selection with feature fusion. Based on our initial result, a Naïve Bayes combiner is used to improve the overall performance further. With this strategy, the accuracy of classification reaches 92.5% for six activities. Additionally, we compare our findings to those of other models utilizing the same database. The results demonstrate that high accuracy can be achieved when adaptive thresholding is used with the SVM method, and computational resources may significantly decrease.

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Multi-Person Recognition Using Separated Micro-Doppler Signatures

Cited by 28 publications

References 16 publications

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

A Convolutional Neural Network-Based Method for Discriminating Shadowed Targets in Frequency-Modulated Continuous-Wave Radar Systems

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Human Activity Classification with Adaptive Thresholding using Radar Micro-Doppler

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