Francesco Fioranelli

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 radar: System requirements and experimental validation

Multistatic radar provides many advantages over conventional monostatic radar, such as enhanced information on target signatures and improvements in detection which are due to the multiple perspectives and differences in the properties of clutter. Furthermore, the fact that receive-only multistatic nodes are passive may be an advantage in military applications. In order to quantify potential performance benefits of these advantages a comprehensive understanding of target and clutter behaviour in multistatic scenarios is necessary. However, such information is currently limited because bistatic and multistatic measurements are difficult to make, their results depend on many variables such as multistatic geometry, frequency, polarization, and many others, and results from previous measurements are likely to be classified for military targets. Multistatic measurements of targets and clutter have been performed over the past few years by the NetRAD system developed at the University College London and the University of Cape Town. A new system, NeXtRAD, is now being developed in order to investigate some of the many aspects of multistatic radar. This paper discusses the results obtained with the previous system and the lessons learnt from its use. These points are then discussed in the context of the new radar, defining key important factors that have to be considered when developing a new multistatic radar system.

Performance Analysis of Centroid and SVD Features for Personnel Recognition Using Multistatic Micro-Doppler

In this letter, we investigate the use of micro-Doppler signatures experimentally recorded by a multistatic radar system to perform recognition of people walking. Three different sets of features are tested, taking into account the impact on the overall classification performance of parameters, such as aspect angle, types of classifier, different values of signal-to-noise ratio, and different ways of exploiting multistatic information. High classification accuracy of above 98% is reported for the most favorable aspect angle, and the benefit of using multistatic data at less favorable angles is discussed.

Through-The-Wall Detection With Gated FMCW Signals Using Optimized Patch-Like and Vivaldi Antennas

This paper presents the design and optimization of patch-like antennas for through-the-wall imaging (TTWI) radar applications in the frequency range 0.5-2 GHz. A basic antenna configuration is analyzed and modified through an optimization aiming at reducing the size of the antenna and focusing the radiated power in a single lobe to be directed toward the wall and the targets to be detected. Both the basic and the optimized antennas have been manufactured and tested. The optimized patch antennas and a conventional Vivaldi antenna have been successfully used in the frequency-modulated interrupted continuous wave (FMICW) radar system developed at Durham University. Results of a novel wall-removal technique for TTWI using numerical simulations and measurements aimed at the detection of stationary targets and people are presented.

Radar and RGB-Depth Sensors for Fall Detection: A Review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

Frequency-Modulated Interrupted Continuous Wave as Wall Removal Technique in Through-the-Wall Imaging

Undesired wall reflections in through-the-wall imaging can mask the return from actual targets and saturate and block the receiver. We propose frequency-modulated interrupted continuous wave (FMICW) signals as a novel wall removal technique and validate its effectiveness through numerical simulations and experiments performed using a radar system built for the purpose. FMICW waveforms appear to mitigate wall reflections and benefit the through-wall detection of stationary targets and of people moving or breathing behind different kinds of wall.

Micro-Doppler based detection and tracking of UAVs with multistatic radar

This paper presents an approach for detection and tracking a micro-UAV using the multistatic radar NetRAD. Experimental trials were performed using NetRAD allowing for analysis of real data to assess the difficulty of detection and tracking of a micro-UAV target. The UAV detection is based on both time domain and micro-Doppler signatures, in order to enhance the discrimination between ground clutter and UAV returns. This micro-Doppler based procedure is shown to improve the clutter/target discrimination, in comparison to a Doppler-shift based procedure. The tracking approach is able to compensate for the limited quality measurement generated by each bistatic pair by fusing the measurements available from multiple bistatic pairs.

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, polarization, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analyzed for two separate problems, namely the classification of unarmed versus 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 behavior, in comparison with the conventional approach of selecting the same features for all nodes.

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.

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

Human micro-Doppler radar signatures have been investigated to classify different types of activities and to identify potential armed personnel in the context of security and surveillance applications. In this paper the use of multistatic micro-Doppler signatures to distinguish between unarmed and armed personnel moving is described. The effect of polarimetry on the classification accuracy is evaluated. Real radar data from a multistatic radar (NetRAD) has been analyzed as part of this work. Suitable features are extracted from the spectrograms generated from the data and then used as input to a classifier. The impact of polarization diversity on the classification performance is investigated, in particular the use of co-polarized or cross-polarized data or their multistatic combination.