Comparison of reduced-rank signal processing techniques

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 performance 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 principal components algorithm, the cross-spectral metric and the multistage Wiener filter. It is shown that the new multistage Wiener filtering technique provides more robust performance as a function of both rank and sample support.