Analysis of adaptive radar systems processing M-sweeps in target multiplicity and clutter boundary environments

Abstract

The problem of multipulse performance evaluation of radar detection systems in nonhomogeneous environments is addressed. The order-statistic (OS)-based algorithms, which set thresholds by processing magnitude-ordered observations within finite moving windows, give a marginal improvement compared with the cell-averaging (CA) procedure in false alarm rate performance in clutter power transitions and due to their immunity to the presence of spurious target returns among the reference cells. Three types of these CFAR detectors are analyzed in this paper. These systems are of the two-window type where they can be used to reduce the large processing time by half against the one-window scheme in ordering the contents of the estimation cells. The considered detectors are: the mean-level (ML) the maximum (MX) and the minimum (MN)-ordered statistics as well as the conventional OS scheme. The performance prediction of these systems employing noncoherent integration for chi-square family of fluctuating targets both in the absence and presence of clutter edges or interfering targets is calculated theoretically. Curves for the detectability loss required thresholds, false alarm rate performance in clutter boundaries and detection performance in multiple target situations are presented with special emphasis on the important Swerling II target fluctuation model. The results show that the ML-OS scheme has the best detection performance and the MX-OS scheme is the preferable one that behaves robust against clutter edges. While the MN-OS scheme does not appear to offer any advantage over others, except in the case where a cluster of radar targets exists among the contents of the reference windows.