|
|
|
“Cumulant-Based Blind Optimum Beamforming”
by Mithat C. Dogan and Jerry M. Mendel
January 1992
Sensor response, location uncertainty and use of sample statistics can severely degrade the performance of optimum beamformers. In this report, we propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher-order-statistics. In this way, we employ the statistical characteristics of the desired signal to make the necessary discrimination, without any a-priori knowledge of array manifold and direction-of-arrival information about the desired signal. We then improve our method to utilize the data in a more efficient manner. In any application, only sample statistics are available, so we propose a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing. We further propose a method that employs both covariance and cumulant information to combat finite sample effects. We analyze the effects of multipath propagation on the reception of the desired signal. We show that even in the presence of coherence, cumulant-based beamformer still behaves as the optimum beamformer that maximizes the Signal to Interference plus Noise Ratio (SINR). Finally, we propose an adaptive version of our algorithm. Simulations demonstrate the excellent performance of our approach in a wide variety of situations.