Exact performance analysis of OS modified versions with noncoherent integration in nonideal situations

The radar signals returning from the targets being illuminated are usually accompanied by thermal noise and clutter. Constant false alarm rate (CFAR) processors are useful for detecting these targets in a background for which the parameters of the statistical distribution are not known and may be nonstationary. The ordered-statistics (OS) CFAR technique has been proven to work satisfactorily in both multiple-target and nonuniform clutter cases. Unfortunately, the large processing time taken by this scheme limits its practical uses. The modified versions of the OS processor have been proposed to replace it in these applications. They can reduce the processing time of the single-window OS detector in half without changing its useful properties. Our goal in this paper is to provide a complete detection analysis for the OS processor along with ordered-statistic greatest-of (OSGO) and ordered-statistic smallest-of (OSSO) modified versions, for M postdetection integrated pulses when the operating environment is nonideal. Analytical results of performance are presented in both multiple-target situations and in regions of clutter power transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the Swerling II target fluctuation model. As the number of noncoherently integrated pulses increases, lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple-target background models. However, the false alarm rate performance of OSSO-CFAR scheme at clutter edges worsens with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries. The OSSO-CFAR scheme is useful in the situation where there is a cluster of radar targets amongst the estimation cells.