This article presents the results of a series of measurements of multistatic radar signatures of small UAVs at L- and X-bands. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. NeXtRAD is capable of recording simultaneous bistatic and monostatic data with baselines and two-way bistatic range of the order of a few kilometres. The paper presents an empirical analysis with range-time plots and micro-Doppler signatures of UAVs and birds of opportunity recorded at several hundred metres of distance. A quantitative analysis of the overall signal-to-noise ratio is presented along with a comparison between the power of the signal scattered from the drone body and blades. A simple study with empirically obtained features and four supervised-learning classifiers for binary drone versus non-drone separation is also presented. The results are encouraging with classification accuracy consistently above 90% using very simple features and classification algorithms.

This chapter has thoroughly explored the necessity and importance of careful planning in the implementation of data fusion methods and architecture within multistatic radar networks. The identification of opportunities for data fusion in a processing system, depicted in this chapter, showcases a multitude of locations for data fusion to occur. These have been progressively integrated into research throughout recent years and is collectively organised, presented and discussed. The works deliberated consisted of the classification of human micro-Doppler signatures, to the characterisation of payloads carried by a micro-drone