Target Multiplicity Performance Analysis of Radar CFAR Detection Techniques for Partially Correlated Chi-Square Targets

• Published in: International journal of electronics and communications Vol. 56; no. 2; pp. 84 - 98
• Main Author: El Mashade, Mohamed B.
• Format: Journal Article
• Language: English
• Published: Stuttgart Elsevier GmbH 2002; Urban & Fischer Verlag
• Subjects: Adaptive detectors; Chi-square fluctuating targets; Multiple-target environments; Postdetection integration; Radar receivers; Postdetection integration; Multiple-target environments; Adaptive detectors; Radar receivers; Chi-square fluctuating targets
• ISSN: 1434-8411; 1618-0399
• DOI: 10.1078/1434-8411-54100077

Most radar targets are complex objects and produce a wide variety of reflections. An important class of targets is represented by the so-called moderately fluctuating Rayleigh targets, which, when illuminated by a coherent pulse train, return a train of correlated pulses with a correlation coefficient in the range 0<ρ<1 (intermediate between SWII and SWI models). The detection of this type of fluctuating targets is therefore of great interest. On the other hand, the CFAR detection is one of the desirable features for radar receivers. Because of the simplicity of cell-averaging (CA detectors in their implementation, they are commonly used in modern radar systems to automatically adapt the detection threshold to the local background noise or clutter power in an attempt to maintain an approximately constant rate of false alarm. In this paper, we analyze the performance of these detectors for the case where the radar receiver postdetection integrates M square-law detected pulses and the signal fluctuation obeys chi-square statistics with two degrees of freedom. These detectors include the mean-level ML), the greatest-of (GO) and the smallest-of (SO) schemes. In these processors, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. Exact formulas for the detection probabilities are derived, in the absence as well as in the presence of spurious targets. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the chi-square fluctuation model with two degrees of freedom. Swerling's well known fluctuation models I and II represent the cases where the signal is completely correlated and completely decorrelated, respectively, from pulse to pulse. Probability of detection curves are presented for the chi-square family of fluctuations, including the Swerling cases I and II. The ML detector has the best homogeneous performance, the SO processor has the best multiple-target performance, while the GO scheme does not offer any merits neither in the absence nor in the presence of outlying targets.