This review explores radar based techniques currently utilised in literature to monitor small UAVs or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration is briefly outlined. Key difficulties involved in the identification and hence tracking of these 'radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and RCS characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum.

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This paper investigates an implementation of an array of distributed neural networks, operating together to classify between unarmed and potentially armed personnel in areas under surveillance using ground based radar. Experimental data collected by the University College London (UCL) multistatic radar system NetRAD is analysed. Neural networks were introduced to the extracted micro-Doppler data in order to classify between the two scenarios, and accuracy above 98% is demonstrated on the validation data, showing an improvement over methodologies based on classifiers, where human intervention is required. The main advantage of using neural networks is to bypass the manual extraction process of handcrafted features from the radar data, where thresholds and parameters need to be tuned by human operators. Different network architectures are explored, from feed-forward networks to stacked auto-encoders, with the advantages of deep topologies being capable of classifying the spectrograms (Doppler-time patterns) directly. Significant parameters concerning the actual deployment of the networks are also investigated, for example the dwell time (i.e. how long the radar needs to stare at a target in order to achieve classification), and the robustness of the networks in classifying data from new people, whose signatures were unseen during the training stage. Finally, a data ensembling technique is also presented which utilises a weighted decision approach, established beforehand, utilising information from all three sensors, and yielding stable classification accuracies of 99% or more, across all monitored zones.

This paper investigates the classification performance of using multistatic human micro-Doppler radar data that have been degraded by some form of jamming. Two simple cases of Signal-to-Noise Ratio (SNR) degradation and nulling of a subset of the available radar pulses are considered for these initial results, leaving more complex forms of jamming for future work. Experimental data collected with a multistatic radar are used in this study, aiming to classify 7 similar human activities, when individual subjects are walking carrying different objects. The results show that the use of multistatic radar data can provide resilience to the effect of the data degradation, thanks to the redundancy and additional information available from multiple radar nodes.

Micro-Doppler signatures are extremely valuable in the classification of a wide range of targets. This work investigates the effects of jamming on micro-Doppler classification performance and explores a potential deep topology enabling low bandwidth data fusion between nodes in a multistatic radar network. The topology is based on an array of three independent deep neural networks (DNNs) functioning cooperatively to achieve joint classification. In addition to this, a further DNN is trained to detect the presence of jamming and from this it attempts to remedy the degradation effects in the data fusion process. This is applied to real experimental data gathered with the multistatic radar system NetRAD, of a human operating with seven combinations of holding a rifle-like object and a heavy backpack which is slung on their shoulders. The resilience of the proposed network is tested by applying synthetic jamming signals into specific radar nodes and observing the networks' ability to respond to these undesired effects. The results of this are compared with a traditional voting system topology, serving as a convenient baseline for this work.

This paper presents initial results on the characterization of radar signatures of wind turbines, in particular larger wind turbines (capacity over 7 MW) used for offshore wind farms. Experimental results from simultaneous data collected using a passive DVB-T (Digital Video Broadcasting-Terrestrial) radar sensor and an active radar working at S-band are presented, as well as some comments on the parallel work on the modelling of the turbine and on the development of detection algorithms specific for this type of clutter. The initial results show significant variability of the signatures for different radar sensors used, but also for different parameters (e.g. polarization) for the same radar sensor and operational conditions of the turbine (rotation speed, yaw angle).

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