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.

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.

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.

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 different human activities using multistatic micro‐Doppler data and features is considered in this study, 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 a 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 against armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only.

This study discusses the analysis of multistatic micro‐Doppler signatures and related features to distinguish and classify unarmed and potentially armed personnel. The application of radar systems to distinguish different motion types has been previously proposed and this work aims to further investigate the applicability of this in more scenarios. Real data have been collected using a multistatic radar system in a series of experiments involving several individuals performing different movements. Changes in classification accuracy as a function of different aspect angle between the direction in which the target faces and the line‐of‐sight of the radar nodes are analysed. Multiple data fusion methodologies are proposed, showing that significant improvement of the classification accuracy can be achieved when using separate classification at each node followed by a voting procedure to reach the final decision. This is beneficial especially at those aspect angles for which micro‐Doppler detection is less favourable.