Matthew Ritchie

Bio: Matthew Ritchie is an academic researcher from University College London. The author has contributed to research in topics: Radar & Bistatic radar. The author has an hindex of 20, co-authored 85 publications receiving 1454 citations. Previous affiliations of Matthew Ritchie include Defence Science and Technology Laboratory & Tsinghua University.

Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features

Abstract: 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.

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

Abstract: 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.

Micro-drone RCS analysis

Abstract: This paper presents both simulation and experimental results of micro-drone rotor blade electromagnetic scattering. The focus of this work is investigating the variation of these reflections as a function of variables such as polarization, frequency and azimuth angle. The simulation results clearly show a strong variation in scattering with azimuth and frequency. Experimental validation of these variations was investigated and similar trends in azimuth were found. The Doppler components from an operational micro-drone in varying polarizations are shown, these demonstrate the expected signals received by a real radar system.

Classification of loaded/unloaded micro-drones using multistatic radar

Abstract: Preliminary results on the use of multistatic radar and micro-Doppler analysis to detect and discriminate between micro-drones hovering carrying different payloads are presented. Two suitable features related to the centroid of the micro-Doppler signature have been identified and used to perform classification, investigating also the added benefit of using information from a multistatic radar as opposed to a conventional monostatic system. Very good performance with accuracy above 90% has been demonstrated for the classification of hovering micro-drones.

Multistatic micro-Doppler radar feature extraction for classification of unloaded/loaded micro-drones

Abstract: This study presents the use of micro-Doppler signatures collected by a multistatic radar to detect and discriminate between micro-drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the radar cross-section of the micro-drones, as well as the singular value decomposition and centroid of the micro-Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid-based features and multistatic information. For the non-hovering scenarios, classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Abstract: Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

'The Micro-Doppler Effect in Radar' by V.C. Chen

Abstract: This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network

Abstract: In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using radar can be applied to control electronic appliances. Compared with an optical recognition system, radar can work regardless of light conditions and it can be embedded in a case. We classify ten different hand gestures, with only micro-Doppler signatures on spectrograms without range information. The ten gestures, which included swiping from left to right, swiping from right to left, rotating clockwise, rotating counterclockwise, pushing, double pushing, holding, and double holding, were measured using Doppler radar and their spectrograms investigated. A DCNN was employed to classify the spectrograms, with 90% of the data utilized for training and the remaining 10% for validation. After five-fold validation, the classification accuracy of the proposed method was found to be 85.6%. With seven gestures, the accuracy increased to 93.1%.

Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

Abstract: Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar—17.3% improvement over SVM.

Radar-Based Human-Motion Recognition With Deep Learning: Promising applications for indoor monitoring

Abstract: Deep learning (DL) has shown tremendous promise in radar applications that involve target classification and imaging. In the field of indoor monitoring, researchers have shown an interest in DL for classifying daily human activities, detecting falls, and monitoring gait abnormalities. Driving this interest are emerging applications related to smart and secure homes, assisted living, and medical diagnosis. The success of DL in providing an accurate real-time accounting of observed humanmotion articulations fundamentally depends on the neural network structure, input data representation, and proper training. This article puts DL in the context of data-driven approaches for motion classification and compares its performance with other approaches employing handcrafted features. We discuss recent proposed enhancements of DL classification performance and report on important challenges and possible future research to realize its full potential.