Abstract
Since the clutter statistics of marine radar are non-stationary and difficult to ascertain, the constant false-alarm rate (CFAR) processor based on some clutter statistical characteristics is hard to obtain the CFAR performance. Especially, for clutter with long smearing effect characteristics, such as lognormal distribution, Pareto distribution, and K distribution, it is difficult to obtain CFAR characteristics using conventional CFAR processing techniques. The main consideration of this paper is to improve the robustness of CFAR, and the Comp-CFAR method is proposed according to the central limit theorem and the logarithmic compression principle of the signal. This method mainly includes clutter two-parameter logarithmic compression processing and accumulation of the magnitudes' average comprehensive CFAR processing. The experimental verification of CFAR characteristics and target detection performance with CFAR in four typical clutter environments shows that this method has better detection ability compared with the NCI-CFAR.
Highlights
Due to the effectiveness of Constant False-Alarm Rate (CFAR) detector, it has been generally used for controlling the false alarm rate of radar to avoid the receiver fault caused by high false alarm rate [1]
The most popular CFAR detection processor is called Mean Level (ML), which is based on Cell Averaging False Alarm Rate (CA-CFAR) [11]–[14]
In order to effectively improve the adaptability of CFAR algorithm to multiple clutter in Gaussian and non-Gaussian clutter background, and to eliminate the influence of large target interference and clutter non-uniformity on CFAR, this paper proposes a comprehensive CFAR processing method (Comp -CFAR)
Summary
Due to the effectiveness of Constant False-Alarm Rate (CFAR) detector, it has been generally used for controlling the false alarm rate of radar to avoid the receiver fault caused by high false alarm rate [1]. Under the background of uniform and non-uniform clutter, more robust CFAR detection performance can be obtained In this method, the identification of target interference and the uniformity of clutter distribution completed uses the method in [22], using VI analysis to determine whether there is target interference in the reference window, using MR test to inspect the uniformity of clutter distribution, and determine the best algorithm for parameter estimation of the test statistic. (2) The results of target interference detection and clutter uniform recognition, as well as the knowledge of target detection are used to realize the adaptive control of the widths of CFAR reference window and protection widow, and to select the best CFAR algorithm It can get more robust detection performance, and is more suitable for algorithm and software implementation compared with the method in [19].
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