This paper compares several reduced-rank signal processing algorithms for adaptive sensor array processing. The comparisons presented here use Monte Carlo analysis to evaluate the algorithmic pegormance as a function of both rank and sample support when the covariance matrix is unknown and estimated from collected sensor data. The adaptive techniques considered are the principalcomponents algorithm, the cross-spectral metric and the multistage Wiener jiltel: It is shown that the new multistage Wiener jiltering technique provides more robust performance as a function of both rank and sample support.
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