Analytical performance evaluation of adaptive detection of fluctuating radar targets
Keywords:fixed threshold detector, adaptive threshold detector, post-detection integration, fluctuating targets, Swerling model, multiple-target situation
A radar target whose return varies up and down in amplitude as a function of time represents the basis of a large number of real targets. This paper is intended to provide a complete analysis of CFAR detection of fluctuating targets when the radar receiver post-detection integrates M returned pulses from χ2 fluctuating targets with two and four degrees of freedom and operates in a non-ideal environment. Owing to the importance of Swerling models in representing a large number of such type of radar targets, we are interested here in adaptive detection of this class of fluctuation models. Swerling cases I and III represent scan-to-scan fluctuating targets, while cases II and IV represent fast pulse-to-pulse fluctuation. Exact expressions of detection probability are derived for all of these models. A simple and an effective procedure for calculating the detection performance of both fixed-threshold and adaptive-threshold algorithms is obtained. In the CFAR case, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. The performance of this detector is analyzed in the cases when the operating environment is ideal and when it includes some of spurious targets along with the target of interest. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the four Swerling’s models cited above. The numerical results show that for strength target return the processor detection performance is highest in the case of SWIV model while it attains its minimum level of detection in the case of SWI model. Moreover, SWII model has higher performance than the SWIII representation of fluctuating targets. For weak target return, on the other hand, the reverse of this behavior is occurred. This observation is common for both fixed-threshold or for adaptive-threshold algorithms.
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