Robust adaptive beamforming method based on shrinkage estimation covariance matrix reconstruction

Abstract

The invention discloses a robust adaptive beamforming method based on shrinkage estimation covariance matrix reconstruction. The method comprises the following steps: performing linear shrinkage estimation on a sampling covariance matrix; performing feature decomposition on the sampling covariance matrix obtained by linear shrinkage estimation to obtain a noise subspace; calculating average noisepower in an expected small-angle interval according to the MUSIC spectrum, and removing the average noise power in the process of reconstructing an accurate expected signal covariance matrix; carryingout feature decomposition on the reconstructed expected signal covariance matrix, taking a feature vector corresponding to a maximum feature value as an initially estimated expected signal steering vector, and combining a modulus constraint and a noise power constraint of the preliminarily estimated expected signal steering vector into a Laplace constraint to obtain a corrected expected signal steering vector; and forming a robust adaptive wave beam by utilizing the sampling covariance matrix obtained by linear contraction and the expected signal steering vector. The main lobe wave beam and the output signal to interference plus noise ratio of the wave beam formed by the method are closer to ideal values, and the method has higher anti-interference capability.

Description

technical field [0001] The invention belongs to the technical field of robust beamforming for array signal processing, in particular to a robust adaptive beamforming method based on shrinkage estimation covariance matrix reconstruction. Background technique [0002] Adaptive beamforming means that the beamformer adaptively forms the weighting factors of each array element according to the change of the signal environment, so as to enhance the desired signal and filter out the interference and noise that affect the desired signal. Adaptive beamforming is an important technology in array signal processing, which is widely used in many fields such as radar, wireless communication, navigation, sonar, seismic monitoring and biomedical engineering. In practical applications, there are usually various errors and non-ideal factors, which lead to a sharp decline in the performance of the beamformer. With the development of beamforming technology and the needs of practical applicatio...

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Problems solved by technology

But this technique also cannot solve the error in the steering vector

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