In this paper, we discuss an "open radar initiative" aimed at promoting the sharing of radar datasets and a common framework for acquiring data. The framework is based on widely available and affordable short-range radar hardware (automotive FMCW radar transceivers). This framework and initiative are intended to create and promote access to a common shared dataset for the development and benchmarking of algorithms. While this is the norm for image processing and speech processing research, there has been reluctance in the radar community so far to create common datasets of shared data, often due to justified intellectual property or security classification reasons. Notable exceptions do exist, such as the MSTAR dataset of SAR images, which enabled great progress across the radar research community for a number of years. With this initiative, we hope to stimulate discussion and, with time, changes of practice in the radar research community. The main contribution of this work relative to previously shared datasets of radar data is that the proposed framework consists of a complete, integrated and replicable hardware and software pipeline, allowing users to not only download existing data, but also to acquire their own data with a compatible format that allows expansion and enrichment of the common dataset.@en

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@en

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.@en