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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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Waveform diversity techniques for radar have gained considerable interest over the past several years. Novel radar waveforms have been proposed to improve detection performance and metric accuracy (i.e., angle estimation performance). This paper explores the potential for using a waveform diversity technique known as Multiple Input, Multiple Output (MIMO) radar to improve the detection performance of slow moving surface targets from a moving radar platform. The MIMO radar system achieves superior performance by transmitting unique uncorrelated waveforms from each antenna subaperture as opposed to the traditional approach of transmitting a single coherent waveform across the entire aperture. The results show that the radar system minimum detectable velocity (MDV) can be reduced by exploiting the ability of a MIMO system to effectively increase the radar antenna aperture.