Analysis of polarimetric multistatic human micro-Doppler classification of armed/unarmed personnel

Fioranelli, Francesco; Ritchie, Matthew; Griffiths, Hugh

doi:10.1109/radar.2015.7131038

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…Most systems use information extracted from the time-frequency distribution (TFD) of radar echoes. In [6], [8], and [9], the features for human motion classification are empirically estimated from the spectrogram. In [10], a set of features is evaluated by using the singular value decomposition (SVD) on the spectrograms and estimating the standard deviation of the first right singular vector.…”

Section: Introductionmentioning

confidence: 99%

On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

Persico

Clemente

Gaglione

et al. 2017

IEEE Trans. Aerosp. Electron. Syst.

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The ability to discriminate between ballistic missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defense system can intercept the missile is very short with respect to target velocities, it is fundamental to Manuscript

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Section: Introductionmentioning

confidence: 99%

On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

Persico

Clemente

Gaglione

et al. 2017

IEEE Trans. Aerosp. Electron. Syst.

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“…Moreover, micro-Doppler features have been often exploited in several aspects of human recognition, such as arm motion analysis [12], identification of target human motions [13], or to distinguish people walking in a noisy background [14]. Low power Frequency Modulated Continuous Wave (FMCW) radar and micro-Doppler tracks have been recently used with various scopes, such as discriminating armed from unarmed people [15], identifying people on the basis of their gait characteristics [16][17][18] or their movements [19], and for gestures recognition [20]. Moreover, radar technology has been successfully applied to the medical field [21,22], for example to remotely monitor the cardiac and respiratory frequency [23].…”

Section: Related Workmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

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“…The research goal is to identify and alert human action behaviors based on the prior behavioral information, it plays a certain predictive role, mainly for hospital patient monitoring, urban struggles, and search & rescue operations. With the smaller and smaller cost of the software defined and wireless radar platform, radar-based human motion recognition research can distinguish different types of motions that are similar to each other, and the application of indoor radar human motion recognition has become a reality [2].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Jiang

Zhou

Che

et al. 2019

Sensors

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As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

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Analysis of polarimetric multistatic human micro-Doppler classification of armed/unarmed personnel

Cited by 16 publications

References 19 publications

On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

People Walking Classification Using Automotive Radar

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

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