In this article, a modified complex-valued FastICA algorithm is utilized to extract the specific feature of the Gaussian noise component from mixtures so that the estimated component is as independent as possible to the other non-Gaussian signal components. Once the noise basis vector is obtained, we can estimate direction of arrival by searching the array manifold for direction vectors, which are as orthogonal as possible to the estimated noise basis vector especially for highly correlated signals with closely spaced direction. Superior resolution capabilities achieved with the proposed method in comparison with the conventional multiple signal classification (MUSIC) method, the spatial smoothing MUSIC method, and the signal subspace scaled MUSIC method are shown by simulation results.
This paper deals with direction of arrival (DOA) and direction of departure (DOD) estimation of existing targets based on polynomial root finding estimators with minimum variance distortionless response (MVDR) criterion in bistatic multi-input multioutput radar systems. First, the presented estimator transforms the conventional two-dimensional searching approach into double one-dimensional polynomial root-MVDR approach for DOA and DOD estimation. Thus, the pairing operation can be obtained automatically. Second, to mitigate the influence of noise corruption and the estimate radial bias, we also presented a differential polynomial root-MVDR estimator to obtain more accuracy estimation performance. Finally, simulation results are provided to verify the efficiency of the proposed estimators.
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