Burr Distribution for X-Band Maritime Surveillance Radar Clutter

Abstract

Recent research has shown that the Pareto family of distributions provides suitable intensity models for high resolution X-band maritime surveillance radar clutter. In particular, the two parameter Pareto Type II model has been shown to fit the Australian Defence Science and Technology Group's medium to high grazing angle clutter returns very well. The Pareto Type II model is a special case of a Burr distributional model, which is a three parameter power law statistical model. Hence this paper begins by investigating the fitting of the Burr model to real data. Based upon these results a detailed study of the development of non-coherent sliding window detectors is justified, for operation in such clutter. Several different approaches will be applied to construct the decision rules. These include a transformation approach and direct adaptation of such detectors, designed for operation in exponentially distributed clutter, to the Burr clutter setting. In addition to this, the fact that the Burr distribution is invariant with respect to two of its distributional parameters allows specification of detection processes which have the constant false alarm rate property with respect to these model parameters. Performance analysis, in simulated clutter, of the derived detectors is then examined. This includes performance in the presence of interference and false alarm regulation during clutter power transitions. This is complemented by an application of the decision rules to target detection in real high resolution X-band maritime surveillance radar clutter.

Multi-polarization time-series of Sentinel-1 SAR imagery to analyze variations of reservoirs’ water-body

“…This is also quantitatively confirmed by the Kolmogorov-Smirnov binary hypothesis test undertaken using a significance level equal to 0.05 [22], [23]. Accordingly, the relationship between the probability of false alarm P fa and the CFAR threshold th is given by [21]…”

“…1(c) and (d), are shown in Fig. 3(a) and (b), respectively, where, for reference purposes, the Burr probability density function (pdf) [21] is also depicted in red. It can be noted that, in both cases, the empirical distribution is well approximated by the Burr pdf.…”

Sea-Surface Slow Small Target Detection Based on Polarimetric Multi-Domain Feature Fusion

“…It is prone to overshadow the echo signal of small targets and undermine the detection performance of radar [1]. In terms of the sea clutter under the illumination of a high-resolution radar with a low gazing angle, its amplitude distribution obtained by statistical methods is non-Gaussian, non-stationary, nonlinear, and with a long trail [2][3][4]. Thus, it is difficult for modelling.…”