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Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.

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Classification of targets using their micro-Doppler signatures has attracted a growing interest in recent years. In addition to their main bulk translation, targets may exhibit additional motions, such as vibrations and rotations, which generate Doppler modulations in the echo that contain unique target features and thus can be used to perform target recognition. Although target classification by micro-Doppler signatures has been exploited in the radio frequency regime for radar systems, much less work has been done in acoustic. In this work, an ultrasound radar operating at 80 kHz has been developed to gather micro-Doppler signatures of personnel targets performing various actions. The performance of a range of classifiers and feature extraction algorithms in distinguishing between these micro-Doppler signatures is presented.

The potential for using micro-Doppler signatures as a basis for distinguishing between aided and unaided gaits is considered in this paper for the purpose of characterizing normal elderly gait and assessment of patient recovery. In particular, five different classes of mobility are considered: normal unaided walking, walking with a limp, walking using a cane or tripod, walking with a walker, and using a wheelchair. This presents a challenging classification problem as the differences in micro-Doppler for these activities can be quite slight. Within this context, the performance of four different radar and sonar systems-a 40 kHz sonar, a 5.8 GHz wireless pulsed Doppler radar mote, a 10 GHz X-band CW radar, and a 24 GHz CW radar-is evaluated using a broad range of features. Performance improvements using feature selection is addressed as well as the impact on performance of sensor placement and potential occlusion due to household objects. Results show that nearly 80% correct classification can be achieved with 10 second observations from the 24 GHz CW radar, while 86% performance can be achieved with 5 second observations of sonar.

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Understanding the decision-making process of deep learning networks is a key challenge which has rarely been investigated for Synthetic Aperture Radar (SAR) images. In this paper, a set of new analytical tools is proposed and applied to a Convolutional Neural Network (CNN) handling Automatic Target Recognition (ATR) on two SAR datasets containing military targets. Firstly, an analysis of the respective influence of target, shadow and background areas on classification performance is carried out. The shadow appears to be the least used portion of the image affecting the decision process, compared to the target and clutter, respectively. Secondly, the location of the most influential features is determined with classification maps obtained by systematically hiding specific target parts and registering the associated classification rate (CR) relative to the images to be classified. The location of the image areas without which classification fails is target type and orientation specific. Nonetheless, a strong contribution of specific parts of the target, such as the target top and the areas facing the radar, is noticed. Lastly, results show that features are increasingly activated along the CNN depth according to the target type and its orientation, even though target orientation is absent from the loss function.

This paper presents the results of recent measurements taken with two radar systems to measure the simultaneous monostatic and bistatic signature of wind turbines, at S-band and X-band. Coherent monostatic and bistatic data was collected with the University College London (UCL) NetRAD 2.4 GHz radar, and the Cranfield University CW radar operating at X-band. This initial analysis shows the bistatic Doppler signature of wind turbines and informs on the key differences seen at modest bistatic angles. Polarimetric variations are also analysed via data gathered using co-polarised VV and HH and cross-polarised VH components.