Robust low-sidelobe beam forming method based on reconstruction of covariance matrix

Abstract

The invention discloses a robust low-sidelobe beam forming method based on the reconstruction of a covariance matrix. The method comprises the following steps of 1) sampling a received signal of a radar array to obtain a signal vector; 2) according to sampled data, figuring out the covariance matrix and the spatial spectrum distribution of received data, and reconstructing a covariance matrix of interference and noise; 3) according to the reconstructed covariance matrix and a guide vector, solving a MVDR model added with the auxiliary lobe constraint through a convex optimization method, and obtaining a global optimal weight vector; 4) multiplying the received signal vector with the obtained optimal weight vector to obtain a robust low-sidelobe self-adaptive beam. The self-adaptive beam forming method is good in robustness and low in sidelobe.

Description

technical field [0001] The invention belongs to the technical field of adaptive digital beam forming of digital array radar, in particular to a robust adaptive beam forming method with low sidelobe. 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 goal. When the training data contains the...

AI Technical Summary

Problems solved by technology

When the training data contains the target signal, the performance of the beamformer is particularly degraded. In this case, the diagonal loading algorithm processes the covariance matrix of the sampling data to make it closer to the ideal interference plus noise matrix, that is, at the minimum Adding a regularization term to the objective function of the variance distortion free response (MVDR) beamformer can enhance robustness, but this method lacks a strict theoretical basis to accurately select the optimal loading level

The eigendecomposition beamforming algorithm is based on eigenvalue decomposition and uses the signal subspace characteristics. Although this method can improve robustness, subspace winding occurs at low signal-to-noise ratios, which greatly reduces the performance of the beamformer.

Based on the worst-case optimal beamforming algorithm, this algorithm is actually equivalent to the diagonal loading algorithm, and its uncertain set constant is difficult to determine in different contexts

[0004] The above algorithms always have their own shortcomings, and cannot meet the requirements of low sidelobes while enhancing robustness.

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