Statistical analysis of bistatic and monostatic sea clutter

Abstract

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.

First measurements with NeXtRAD, a polarimetric X/L Band radar network

“…Calibration techniques are applied to find the distortion matrices, co-and cross-talk imbalances, isolation errors and phase errors. These distortions can be removed or minimized to correct the errors and non-idealities introduced by the antennas [20].…”

Multistatic adaptive CFAR detection in non-Gaussian clutter

“…The validity of this model for sea clutter has been discussed by a large number of works in literature for both monostatic [26][27][28] and, more recently, for bistatic clutter [25,29,30]. The rapid fluctuations of sea clutter are taken into account for the speckle component, which is modelled as a complex Gaussian random vector, whose covariance matrix is R = E{xx H }.…”

“…by generating clutter samples distributed according to the K-plus-noise distribution, with unitary clutter power, thermal noise power of 0.1 and different values of the shape parameters (ν 1 , ν 2 ), both included between 0.04 and 1.2 (in real data, the values of the shape parameter are usually included within this interval [30]). The threshold values were set in order to obtain a P FA = 10 −5 , as shown in Table 2.…”

“…In recent works [25,30], the statistical analysis of simultaneously collected bistatic and monostatic data of the shape parameter has highlighted that the monostatic shape parameter is almost constant with range, whereas the bistatic one shows large deviations, due to the geometry change. Hence, the detection performance, achieved by applying the multichannel NAMF to monostatic and bistatic data at different ranges, may be heavily influenced by this clutter diversity, especially by the behaviour of bistatic clutter.…”

“…For each range cell, the number of received pulses is N p = 130,000, corresponding to a total acquisition time of 130 s. In this work, we consider the datasets collected on October 21, 2010, with a baseline of 728 m and a bistatic angle (β) of 30° [25,30].…”