Steering vector and covariance matrix joint iteration robust beam forming method

Abstract

The invention discloses a steering vector and covariance matrix joint iteration robust beam forming method, and solves the problem of the expected signal steering vector and interference-plus-noise covariance matrix estimation in the adaptive beam forming technology. The implementation process is that the sample covariance matrix is estimated by using the radar echo data acquired by the array; theexpected signal steering vector and interference-plus-noise covariance matrix is initialized; the convex optimization equation is established to estimate the expected signal steering vector error; the steering vector and the covariance matrix are updated, and alternating iteration is performed until the optimal estimation of the steering vector and the covariance matrix is obtained; and the weight value is calculated and robust beam forming is realized. The expected signal steering vector constraint capacity is enhanced when the direction of arrival and array calibration errors coexist, the expected signal estimation interference-plus-noise covariance matrix is accurately eliminated from the sample covariance matrix and the 'self-elimination ' phenomenon of the expected signal can be avoided and thus the method can be used for realizing adaptive beam forming under the condition of existence of the direction of arrival and array calibration errors.

Description

technical field [0001] The invention belongs to the field of array signal processing, and mainly relates to the estimation problem of a desired signal steering vector and an interference-plus-noise covariance matrix in adaptive beamforming, specifically a robust beamforming method for joint iteration of steering vectors and covariance matrices, which can be used in arrays Adaptive beamforming under calibration error. Background technique [0002] Adaptive beamforming technology is widely used in aviation, spaceflight, radar and communication systems. It improves the output Signal to Interference and Noise Ratio (SINR) by forming gain in the desired signal direction and null in the interference direction. However, in the actual working environment, there are array element position errors, channel amplitude and phase errors, etc., resulting in deviations in the steering vector constraints of the desired signal. Theoretical studies have shown that when there is constraint devi...

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Problems solved by technology

However, this method needs to know accurate array structure information, that is, only the signal direction of arrival error is considered, and the array element position error, amplitude phase error, etc. are not considered, and there is interference and noise covariance matrix mismatch in the actual working environment

[0008] The diagonal loading method mentioned above can improve the performance of the snapshot, but there is a phenomenon of "self-elimination" of the desired signal under the strong desired signal; the subspace method can improve the estimation performance of the steering vector under the strong desired signal, However, there is a large error under the weak desired signal; the existing constrained optimization methods have limited performance improvement due to the deviation of the desired signal steering vector constraint; the interference noise covariance matrix reconstruction method can be significantly improved when the array is precisely calibrated. Adaptive beamformer performance, but loss of interference rejection under array alignment errors

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