Capibility Analysis Of Doa Estimation Of Subspace Intersection Method

The source bearing , or direction of arrival(DOA), estimation is an important aspect in array signal processing. Using large aperture array, the sonar performance can be improved, and the detecting distance from the target can be increased, but the effect of the multipath nature of sound propagation, or (multimode wave), in ocean on DOA estimation has to be considered as the increase of array aperture. The DOA estimation of subspace intersection method(SI) can effectively suppresses the effect of multipath nature on DOA estimation in large aperture array. Comparing with the matched field processing method, SI needs smaller amount of calculation.The spatial spectrum peaks of DOA estimation method based on plane wave mode usually split or shift because of the effect of multipath nature of sound propagation in the ocean, and the more the bearing is close to the endfire, the more this phenomena is obvious. SI suppresses the effect of multimode nature on DOA estimation effectively, but because of the Array Response Matrix(ARM) runs shot of order and DOA outputs hathpace near the sidefire, being examining blind, and the robustness of SI is not good. limited square subspace intersection method(LSSI) and subspace intersection method based on white noise loading approach(WASI) really improved the robustness of SI, but still depends on SNE, and be complex. This paper expect to find a simple SI method and broad the restriction of SNE.This paper first analyses the capability and the SNE affect of the SI method. Then proves that the arithmetic by itself has some tolerance about SNE, since overestimation of SNE makes some affection of DOA output, but the DOA estimation has little impact. Next aiming at the poor robustness of SI, an Modified subspace intersection method(MSI) is proposed. This method eigendecomposes the covariance matrix of the ARM, rebuilds a full-order New Array Response Matrix(NARM) with the chosen eigenvector, which can avoid the hathpace efficiently. The MSI ride the loss brought by the noise loading, for adding only once decomposion operation, this method is for simpleness. At last simulation experiment is done for analyzing and validating the theoretics performance of MSI; the result shows that the MSI has better performance and more SNR plus than the SI, especially when weak sources exist.