摘要:
本文基于篩選平均(CM)和無偏篩選平均(UCM)提出了兩種改進的恒虛警檢測器MCM-CFAR和MUCM-CFAR,并應(yīng)用了何友(1994)提出的自動篩選技術(shù).在Swerling Ⅱ型目標(biāo)假設(shè)下,并考慮瑞利分布雜波和單脈沖檢測情形,本文推導(dǎo)出了MCM-CFAR和MUCM-CFAR檢測器的Pfa、Pd和平均判決門限(ADT)的解析表達式,并與其它方案進行了比較.分析結(jié)果表明,它們在均勻背景和多目標(biāo)環(huán)境中的性能均明顯優(yōu)于GOSCA和OS;當(dāng)IL=4,IR=2時,MCM-CFAR比OS改善了2dB,MUCM-CFAR也比OS改善了1.5dB;MCM的性能略優(yōu)于CM,MUCM與UCM接近,但它們的樣本排序時間不足CM、UCM和OS的一半,便于工程實現(xiàn).
Abstract:
Two modified censored mean (MCM) and modified unbiased censored mean (MUCM) CFAR algorithms are proposed. Both split the reference window into two sub-windows which apply CM or UCM method to create two local noise power estimations, the mean value of them are taken to set an adaptive threshold. Both use the automatic censoring technique proposed by He You (1994). Under Swerling II assumption, considering Rayleigh distributed noise and single-pulse square-law detection, the analytic expressions of Pfa, Pd and ADT of both are derived. By comparison with other schemes, the results show that their performance are evidently superior to that of GOSCA and OS in homogeneous background and in multiple target situations, in the case of IL=4, IR=2, the CFAR loss of MCM is improved by 2 dB relative to that of OS, that of MUCM is improved by 1.5 dB over OS. The performance of MCM is slightly better than that of CM, the performance of MUCM is the nearly same as that of UCM, but their sample sorting time is less than half of that of CM, UCM and OS.
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