Detection analysis of linearly combined order statistic CFAR algorithms in nonhomogeneous background environments

Abstract

Automatic detection radars require some technique of adaptation against variations in the background clutter in order to control their false alarm rate. Recent interest has focused on order statistic based algorithms where they are quite robust against both interference and clutter edges. The primary purpose of this paper is to analyze the performance of a CFAR procedure that uses a linear combination of order statistics (LCOS) as a test statistic for its noise power level estimation. This processor is more general than the trimmed mean scheme. The LCOS-CFAR algorithm reduces to the cell-averaging (CA), order statistic (OS), censored cell averaging (CCA), censored mean level (CML) and trimmed mean (TM) CFAR algorithms for specific parameter values. The performance of the proposed detector is evaluated in the case where the reference sample set contains abrupt change in clutter power distribution amongst its elements and when the content of the sample set is contaminated with returns from the interferers. Besides providing a complete detection analysis for single pulse detection, this paper extends the single pulse analysis to the LCOS-CFAR processor with noncoherent integration of M-pulses when the background environments are nonhomogeneous. The primary and the secondary extraneous targets are assumed to be fluctuated in accordance with the Swerling II target model and closed-form expressions for the false alarm and detection probabilities are derived for the two cases.