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

Multistatic human micro-Doppler classification of armed/unarmed personnel

Classification of different human activities using multistatic micro-Doppler data and features is considered in this paper, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after Short Time Fourier Transform analysis. The resulting feature vectors were then used as individual, pairs, triplets, and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed vs armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only

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

Compressed sensing of monostatic and multistatic SAR

In this letter, we study the impact of compressed data collections from a synthetic aperture radar (SAR) sensor on the reconstruction quality of a scene of interest. Different monostatic and multistatic SAR measurement configurations produce different Fourier sampling patterns. These patterns reflect different spectral and spatial diversity tradeoffs that must be made during task planning. Compressed sensing theory argues that the mutual coherence of the measurement probes is related to the reconstruction performance of sparse domains. With this motivation, we propose a closely related t%-average mutual coherence parameter as a sensing configuration quality parameter and examine its relationship to the reconstruction behavior of various monostatic and ultranarrow-band multistatic configurations. We investigate how this easily computed metric is related to SAR reconstruction quality

Personnel recognition based on multistatic micro-Doppler and singular value decomposition features

The use of micro-Doppler signatures experimentally collected by a multistatic radar system to recognise and classify different people walking is discussed. A suitable feature based on singular value decomposition of the spectrograms is proposed and tested with different types of classifiers. It is shown that high accuracy of between 97 and 99% can be achieved when multistatic data are used to perform the classification

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

Correlation Analysis of Simultaneously Collected Bistatic and Monostatic Sea Clutter

In this work we analyse the correlation properties of simultaneous bistatic and monostatic polarimetric sea clutter data, collected by the NetRAD multistatic radar system. In particular, we study the temporal autocorrelation and cross-correlation functions of the texture and speckle samples, as a function of the system geometry and of the polarization of the transmitter and receiver antennas. These features can vary significantly as a function of bistatic angle and the goal of this paper is to quantify these variations and extract any trends that are observed

Copolar Calibration of Multistatic Radar in the Presence of Multipath

This paper addresses the Polarimetrie calibration of the nodes of a multistatic radar system, by using a reference object with known scattering matrix, such as a metallic sphere. A calibration technique is proposed and its experimental validation performed in a realistic scenario, by accounting also for the multipath effect. The intensity of the signal scattered by a metallic sphere and received by the monostatic and bistatic nodes of the NetRAD system is measured, by varying the antenna height, the object range and the bistatic angle. The adopted calibration technique shows a quite good accuracy, as the calibrated values of the radar cross section of the reference object are close to the theoretical ones, after the compensation of the multipath effect

Measurements and analysis of multistatic and multimodal micro-Doppler signatures for automatic target classification

The purpose of this paper is to present an experimental trial carried out at the Defence Academy of the United Kingdom to measure simultaneous multistatic and multimodal micro-Doppler signatures of various targets, including humans and flying UAVs. ewline Signatures were gathered using a network of sensors consisting of a CW monostatic radar operating at 10 GHz (X-band) and an ultrasound radar with a monostatic and a bistatic channel operating at 45 kHz and 35 kHz, respectively. A preliminary analysis of automatic target classification performance and a comparison with the radar monostatic case is also presented