In this work, we analyze the sea clutter data collected simultaneously by the bistatic and monostatic nodes of a S-band netted radar system. The analyzed radar system is the NetRad, developed by the University College London (UCL) and the datasets were collected with four different out-of-plane geometries. The aim of the analysis is to compare statistically the behavior of the bistatic and monostatic sea clutter for different geometries. In particular, we focus on the sea spikes, whose presence increases the probability of false alarm of the radar detector. The statistical analysis is carried out by comparing the empirical distribution of sea clutter data with some known heavy-tailed distributions and by studying the behavior of three statistical parameters, the kurtosis the Weibull and K+Noise shape parameter. An algorithm that separates the sea spikes from the Bragg background is implemented, in order to study in-depth the sea spikes statistics. To this aim, we examine the empirical distribution of the spike duration and of the interval between two subsequent spikes. The results of our analyses show that spikiness is higher for low values of the bistatic angle and that bistatic data are sometimes less spiky than monostatic ones only for horizontal polarization.

This work addresses the problem of target detection for multistatic radars. We propose an algorithm that is able to keep constant the false alarm rate, when the disturbance samples associated with each receiver-transmitter pair are distributed according to a compound Gaussian model. The performance of the proposed detection algorithm are analysed to assess the impact of clutter diversity on detection performance. The results show that clutter statistical diversity has a strong impact on detection performance. The performance of both single-channel and multichannel detection schemes are evaluated by processing real sea clutter data collected by the NetRAD nodes, in order to evaluate which of the two channels, i.e. the bistatic and monostatic channels, is more favourable for target detection. Furthermore, the gain achieved by using a multistatic detection algorithm is also analysed.

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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