Beam forming method based on covariance matrix reconstruction and steering vector correction

Abstract

The invention discloses a beam forming method based on covariance matrix reconstruction and steering vector correction. The method comprises steps of (1) sampling a received signal of a radar array toobtain a received signal vector; (2) according to the sampled received signal vector, obtaining a received data covariance matrix and a spatial spectral distribution, and then obtaining an interference steering vector by a spherical constraint method, and reconstructing an interference-noise covariance matrix; (3) correcting a desired signal steering vector according to the reconstructed interference-plus noise covariance matrix; (4) according to the reconstructed covariance matrix and the corrected desired signal steering vector, solving a MVDR model with added side lobe constraints by a convex optimization method to obtain a global optimal weight vector; and (5) multiplying the received signal vector by the global optimal weight vector to obtain a robust low-sidelobe adaptive beam. Thebeam forming method of the present invention not only has good robustness but also has low side lobes.

Description

technical field [0001] The invention belongs to the technical field of adaptive digital beam forming of digital array radar, in particular to a beam forming method based on covariance matrix reconstruction and steering vector correction. Background technique [0002] Adaptive beamforming technology has been widely used in wireless communication, radar, sonar, medical imaging, radio astronomy and other fields. Conventional adaptive beamforming assumes that the exact knowledge of the steering vector of the desired signal is known, but in practice the performance of beamforming is affected by errors, resulting in a serious decline in the performance of the beamformer. In order to correct the deviation, robust adaptive beamforming technology came into being. [0003] For the design of an adaptive beamformer with excellent performance, robustness, sidelobe level control, and interference suppression should be considered, so some technical measures will be used to achieve this goa...

AI Technical Summary

Problems solved by technology

Beamformer performance degrades severely when the training data contains signals of interest resulting in mismatched covariance matrices or mismatched steering vectors

In order to improve the robustness of the beamformer to the covariance matrix mismatch, the diagonal loading algorithm artificially introduces white noise on the diagonal of the sampled data covariance matrix, making it closer to the ideal interference plus noise covariance matrix, and also That is, adding a regularization term to the objective function of the minimum variance distortion free response (MVDR) beamformer, but this method lacks a rigorous theoretical basis for selecting the optimal loading level and the robustness to steering vector mismatch is not improved

The eigenspace-based beamforming algorithm uses the signal subspace characteristics to improve the robustness of the beamformer to the steering vector mismatch, but does not take the covariance matrix mismatch into account, and the subspace will be entangled when the signal-to-noise ratio is low, resulting in beamforming degraded performance

The worst-case optimal beamforming algorithm is actually equivalent to the diagonal loading algorithm, so there is a common defect that its uncertain set constant is difficult to determine in different contexts

[0004] The above algorithms always have their own shortcomings, and cannot enhance the robustness of the beamformer to the mismatch of the covariance matrix and the mismatch of the steering vector at the same time, and do not consider the requirement of low sidelobe

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