摘要
The performance of multiple signal classification （MUSIC） algorithm with regard to solving closely spaced direction of arrivals （DOAs） depends strongly upon the signal-to-noise ratio （SNR） and snapshots. In order to solve this problem, a method by reconstructing the spatial spectrum function with both noise subspace and signal subspace is presented in this paper. The key idea is to apply the full information contained in covariance matrix and change the projection weights of steering vector on the noise and signal subspace by their revised eigenvalues, respectively. Comparing with the MUSIC algorithm, it does not increase any computational complexity either, and remarkably, it has the advantages of simultaneously reducing noise and keeping the high-resolution ability under low SNR and small sample sized scenarios. Simulation and experiment results are included to demonstrate the superior performance of the proposed algorithm.
The performance of multiple signal classification （MUSIC） algorithm with regard to solving closely spaced direction of arrivals （DOAs） depends strongly upon the signal-to-noise ratio （SNR） and snapshots. In order to solve this problem, a method by reconstructing the spatial spectrum function with both noise subspace and signal subspace is presented in this paper. The key idea is to apply the full information contained in covariance matrix and change the projection weights of steering vector on the noise and signal subspace by their revised eigenvalues, respectively. Comparing with the MUSIC algorithm, it does not increase any computational complexity either, and remarkably, it has the advantages of simultaneously reducing noise and keeping the high-resolution ability under low SNR and small sample sized scenarios. Simulation and experiment results are included to demonstrate the superior performance of the proposed algorithm.
基金
supported by the National Basic Research Program of China (61393010101-1)
