Detection of multi‐people micro‐motions based on range–velocity–time points

Du, Heng; Jin, Tian; Li, Meng; Song, Yongping; Dai, Yongpeng

doi:10.1049/el.2019.2419

Cited by 11 publications

(9 citation statements)

References 5 publications

(6 reference statements)

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“…An open problem faced by the radar research community for human monitoring is when multiple people are in the radar field of view and the recognition of activities while subjects are occluded by other subjects or objects. Techniques to separate the signatures of multiple subjects have been proposed using the fine range information of UWB radar [44] or the separation of the scatterers points of multiple subjects in the 3D radar cube [45]. These techniques could help separate and decompose the total signature into individual signatures that can then be subsequently processed by the proposed classification approach.…”

Section: Discussionmentioning

confidence: 99%

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

et al. 2020

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Recognition of human movements with radar for ambient activity monitoring is a developed area of research that yet presents outstanding challenges to address. In real environments, activities and movements are performed with seamless motion, with continuous transitions between activities of different duration and a large range of dynamic motions, compared with discrete activities of fixed-time lengths which are typically analysed in the literature. This paper proposes a novel approach based on recurrent LSTM and Bi-LSTM network architectures for continuous activity monitoring and classification. This approach uses radar data in the form of a continuous temporal sequence of micro-Doppler or range-time information, differently from from other conventional approaches based on convolutional networks that interpret the radar data as images. Experimental radar data involving 15 participants and different sequences of 6 actions are used to validate the proposed approach. It is demonstrated that using the Dopplerdomain data together with the Bi-LSTM network and an optimal learning rate can achieve over 90% mean accuracy, whereas range-domain data only achieved approximately 76%. The details of the network architectures, insights in their behaviour as a function of key hyper-parameters such as the learning rate, and a discussion on their performance across are provided in the paper.

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Section: Discussionmentioning

confidence: 99%

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

et al. 2020

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“…In Equation (15), the image matrix X ξ does not consist of strong scattering point signal components. We define X ξ after the pixel-wise adaptive Wiener filter is X w2 .…”

Section: Range-frequency-time Radar Data Cube Constructionmentioning

confidence: 99%

“…Compared to 2D radar images, range-frequency-time joint-variable representations are more effective in target parameter estimation because they offer the possibility of using comprehensive m-D information. Several multidimensional processing techniques have been developed over the past few years [15][16][17][18][19]. For example, He Y et al proposed a novel radar signal concept called range-Doppler surface (RDS), which can contain range, frequency, and time information [17].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Precession Cone-Shaped Targets Based on Range–Frequency–Time Radar Data Cube

Han

Feng

2022

Remote Sensing

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A radar echo signal received from a cone-shaped target with precession contains micro-Doppler (m-D) information from different effective scattering centers. By taking full advantage of the m-D information, this paper proposes a parameter estimation algorithm for precession cone-shaped targets based on the range–frequency–time radar data cube (RDC). We build scattering center models of precession cone-shaped targets with the occlusion effect. The Binary Mask method is first utilized to obtain a high-resolution range-Doppler (RD) sequence. On this basis, the range–frequency–time RDC can be extracted from the RD sequence. In order to approach the actual case, we discuss the parameter estimation algorithm under different radar lines-of-sight (LOS). The most attractive attribute of this algorithm is that it can conduct in-depth research on m-D parameter estimation from a three-dimensional (3D) domain. Finally, the experimental results illustrate the effectiveness of the proposed method.

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“…In [10], the authors combined the range information and the micro-Doppler signature, with deep learning, improving the accuracy of the fall detection to approximately 98 %. In [11], the authors designed a new 3D model, range-velocity-time points, to describe micro-motions under multi-target conditions. Several classification techniques such as support vector machine and linear discriminant analysis were compared with the author's method in [8], and the conclusions that both support vector machine and Naïve Bayes algorithm are sufficient to distinguish micro-Doppler signatures of different activities.…”

Section: Introductionmentioning

confidence: 99%

Multi-domains based human activity classification in radar

Li¹,

Fioranelli²,

Yang³

et al. 2021

IET International Radar Conference (IET IRC 2020)

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In human activity recognition (HAR) based on radar, significant research exists on statistical features extracted from the spectrogram (µD), whereas the research which considers other domains is less developed. This paper is aimed to investigate three domains of radar data: µD, Cadence Velocity Diagram (CVD), and range-time (RT) information, evaluating which ones are best suited to classify specific activities. In addition, information fusion is applied to enhance classification accuracy and compare it with the results of single domain approach. Based on the previous results, a hierarchical structure is proposed to improve the performance of classification further. The preliminary results show that different domains have distinctive sensitivity to specific activities. RT information is sensitive to the moving target crossing range bins, while CVD is more sensitive to body movement. The µD is more balanced, which means it can observe both moving targets and body movements. Furthermore, improvement in accuracy is approximately 6-23 % using feature-level fusion. A hierarchical classification approach is also investigated, which has accuracy in the order of approximately 92 %.

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Detection of multi‐people micro‐motions based on range–velocity–time points

Cited by 11 publications

References 5 publications

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

Parameter Estimation for Precession Cone-Shaped Targets Based on Range–Frequency–Time Radar Data Cube

Multi-domains based human activity classification in radar

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