Classification of unarmed/armed personnel using the NetRAD multistatic radar for micro-Doppler and singular value decomposition features

In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter

Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

Monostatic and Bistatic Radar Measurements of Birds and Micro-drone

This paper analyses the experimental results from recent monostatic and bistatic radar measurements of multiple birds as well as a quadcopter micro-drone. The radar system deployed for these measurements was the UCL developed NetRAD system. The aim of this work is to evaluate the key differences observed by a radar system between different birds and a micro-drone. Measurements are presented from simultaneous monostatic co/cross polarized data as well as co-polar bistatic data. The results obtained show comparable signature within the time domain and a marked difference in the Doppler domain, from the various birds in comparison to the micro-drone. The wing beat properties of the birds are shown for some cases which is a stark contrast to the rotor blade micro-Doppler signatures of the drone

Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks

In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node

Feature diversity for optimized human micro-doppler classification using multistatic radar

This paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes

Origin and structure of Devensian depressions at Letton, Herefordshire

Groups of circular to oval enclosed depressions in soft sediments of Pleistocene age are relatively common in north-west Europe. These features are normally interpreted as being either glacial or periglacial in origin. Where these features are developed in glacial sediments, a glacial (and specifically ‘kettle hole’) genesis is considered most likely. Some groups of features, however, have been re-interpreted as being periglacial in origin and are thought to be the remains of cryogenic mounds (former pingos or palsas/lithalsas). The problem at many sites, of course, is correct identification and previously this was often resolved through extensive trenching of the sediments. The use of geophysics in the form of electrical resistivity tomography and ground probing radar, however, can aid investigation and interpretation and is less invasive. A group of enclosed depressions in the Letton area of Herefordshire within the Last Glacial Maximum ice limit (Late Devensian) have been investigated in this way. The morphology and internal structure of the features and their existence in glaciolacustrine sediments of Late Devensian age strongly suggests that these depressions are kettle holes resulting from ice block discharge into a shallow lakes or lakes, and hence a glacial origin is supported. The lack of any ramparts surrounding the depressions (at the surface or any evidence of these at depth) and the fact that they do not overlap (‘mutually interfere’) indicates that they are not the remains of cryogenic mounds