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Asymptotic performance analysis of the conjugate gradient reduced-rank adaptive detector

We consider an adaptive reduced-rank CG-AMF detector, obtained by using the conjugate gradient (CG) algorithm to solve for the weight vector of the adaptive matched filter (AMF). We examine the output signal-to-interference-and-noise ratio (SINR) of the CG-AMF detector in the presence of strong clutter/interference. An asymtotic expression of the probability density function of the output SINR is obtained. Numerical results show that for a fixed training size, the CG-AMF detector often reaches its peak output SINR with a lower rank compared with the other reduced-rank detectors, which implies that the CG-AMF detector has lower computational complexity and less training requirement.