Matrix-filtering-based sparse asymptotic minimum variance direction-of-arrival estimation method

Abstract

The invention relates to a matrix-filtering-based sparse asymptotic minimum variance direction-of-arrival (DOA) estimation method. A matrix filter is used as a preprocessor to process an array receiving signal, so that a strong interference signal is suppressed and thus a phenomenon that a weak target is shielded by the strong interference signal or the positioning precision of the weak target bythe subsequent DOA estimation algorithm is affected by the strong interference signal is avoided. DOA estimation is carried out base on a sparse asymptotic minimum variance (SAMV) algorithm, so that the high-resolution performance is kept under the circumstances of small snapshot and low signal-to-noise ratio and a direction-of-arrival estimation problem of a coherent signal is solved. Meanwhile,the algorithm only needs to provide a threshold value eta of iterative stopping, thereby avoiding selection of regular parameters and enhancing the practicability of the algorithm. Besides, while thearray manifold change of the interference destroys the structure of the covariance matrix, the DOA estimation of the weak target signal still can be realized by the SAMV algorithm.

Description

technical field [0001] The invention relates to the fields of signal processing and the like, and relates to a sparse approximate minimum variance orientation estimation method based on matrix filtering, which is applicable to the orientation estimation of weak target signals in a strong interference environment, and relates to the fields of signal processing and the like. Background technique [0002] Passive sonar is an effective tool for underwater target orientation (Direction of Arrival, DOA) estimation. Unlike active sonar, which transmits signals autonomously and detects targets by receiving reflected echoes, passive sonar detects targets by receiving ship radiation noise, so it has better concealment. However, when there is a strong interference sound source such as tugboat noise around the target signal, the strong interference sound source will affect the positioning accuracy of the DOA estimation algorithm for the weak target signal and even mask the weak target s...

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Problems solved by technology

However, the SpSF-MF algorithm needs to provide a regular parameter when solving the solution. Because the selection of this parameter is usually difficult, it is difficult to apply SpSF-MF in actual signal processing.

In addition, the array manifold will change after the interference passes through the matrix filter. When the interference power output by the matrix filter is still stronger than the weak target signal, the structure of the covariance matrix of the output signal will be due to the change of the interference array manifold is destroyed, which will cause the failure of the SpSF-MF algorithm

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