NeXtRAD is a polarimetric, L and X Band, multistatic (three nodes), pulse Doppler radar, developed by UCT and UCL, as a follow on to the NetRAD sensor. This paper reports on the trials carried out in 2018, mostly in Simon's Bay, South Africa. The sensors (one active, two passive) are connected by WiFi communications link, with a maximum separation of 40 km. Practically, results are reported with 8 km maximum baselines. The focus is on targets in sea clutter and micro-Doppler. We report on the final integration and test of the system command and control system that allows for scheduling of measurement and recording of bursts of pulses, as well as video of the radar field of view. Some innovations have been made in terms of digital hardware, firmware, and high performance computing technology. The system is synchronised with the UCT GPS Disciplined Oscillators (one per node), but we also report on bistatic measurements with White Rabbit, fibre timing system, as well as the consequences of GPS failure (GPS Denied Environment).
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 is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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