Inhomogeneous Analysis of Novel Model of CFAR Approaches to Detect Two-Degrees of Freedom Partially-Correlated χ2-Targets

Abstract

Reliable and high performance radar systems have ubiquitous demand. The operation of such systems is affected by the presence of natural and artificial noise sources. One of the basic radar concepts is to decide whether the target is present or not. Meanwhile, the general objective of all radar detection schemes is to ensure that false alarms don't fluctuate randomly. Thus, to cope with an inhomogeneous changing clutter environment, it is beneficial to be able to detect both high- and low-fidelity targets while maintaining the rate of false alarm fixed. This calls for an adaptive thresholding strategy that vary the detection threshold as a function of the sensed environment, and most modern radars implement this approach automatically. The feature of constant false alarm rate (CFAR) activates the threshold in such a way that it becomes adaptive to the local clutter environment. Many alternatives have been proposed to achieve such demanded property. Owing to the diversity of the radar search environment (target multiplicity & clutter edges), there exists no universal CFAR procedure. This prompts the necessity to investigate the composite architecture as a novel strategy. The goal of this paper is to analyze the fusion of CA, OS, and TM processors in post-detection integration of M-pulses. The primary and outlying targets are assumed to obey χ 2 -distribution with two-degrees of freedom in their fluctuation. Closed-form expression is derived for the detection performance. Our simulation results show robust behavior of the new model in the absence as well as in the presence of outlying targets. In addition, a significant improvement of the detection performance of novel strategy over the individual CFAR detectors is noticed. Moreover, the outweighing, over Neyman-Pearson (N-P) detector, of the fusion model, in ideal background, is evidently demonstrated. This ability to obtain improved performance compared to existing models is the major contribution of this work.