Self-adapting beam forming method based on reconstitution of covariance matrix

Abstract

The invention discloses a self-adapting beam forming method based on reconstitution of a covariance matrix. The method comprises the steps of building a mathematical model with M array element and receiving signals in Q different directions by uniform linear arrays, wherein a desired signal to be detected and Q-1 interference signals are included; then acquiring an interference and noise sampling covariance matrix of a sample matrix to perform eigenvalue decomposition, thus respectively acquiring M eigenvalues sorted in a descending order after eigenvalue decomposition, M feature vectors corresponding to the M eigenvalues, and a noise power estimated value; sequentially computing Q-1 interference signal incident angle estimated values and estimated values of Q-1 interference signal steering vectors of the Q-1 interference signal incident angle estimated values; then acquiring an optimized interference and noise sampling covariance matrix of components, not in an incidence direction of [theta]0, of the desired signal to be detected; and at last computing a self-adapting processor weight vector based on the reconstitution of the covariance matrix.

Description

technical field [0001] The invention belongs to the field of array signal processing, and in particular relates to an adaptive beamforming method based on covariance matrix reconstruction, which is suitable for the design of adaptive beamformers in signal processing systems in which signals received by array antennas such as communication systems contain expected signals . Background technique [0002] Array signal processing has a wide range of applications in communication, radar, sonar, electronic countermeasures, medical imaging, radio astronomy and other fields. Beamforming technology is an important branch of array signal processing. With the popularity of phased array antennas in radar and communication systems, beamforming technologies and methods have also undergone rapid development and improvement. The original beamforming technology applies different feed phases to different array sensors so that the output phase of the complex signal received by the array anten...

AI Technical Summary

Problems solved by technology

[0003] Although the traditional beamforming method can form a high gain in the required direction, it does not have the function of suppressing the interference signal; in the 1960s, the minimum variance distortion-free response (MVDR) beamformer proposed by Capon was deduced through the theory The method of calculating the adaptive weight of the array based on the interference-plus-noise covariance matrix, thus forming a response null in the interference direction while ensuring the gain of the signal in the desired direction, theoretically can effectively suppress the interference signal; The sampled matrix inversion (SMI) beamformer, which uses the received signal sampling covariance matrix instead of the theoretical interference plus noise covariance matrix, can adaptively suppress interference according to the received signal of the array antenna, and is a classic adaptive beamformer.

[0004] The current research on beamforming methods mainly focuses on the case where the sampling covariance matrix of the received signal contains expected signal components. When considering the array amplitude and phase errors, signal observation errors and array calibration errors in actual engineering, the expectation in the sampling covariance matrix The existence of signal components makes the adaptive weights to be solved produce a destructive response to the desired signal, which leads to a significant decline in the performance of the adaptive beamformer; in response to this problem, Vorobyov proposed the optimal equation based on the elliptic uncertain set optimization equation in 2003. The poor performance optimization method, to a certain extent, reduces the influence of the expected signal component in the sampling covariance matrix on the performance of the adaptive beamformer. However, the performance of this method is affected by the prior parameters, and the signal with high signal-to-noise ratio Poor performance in the environment

In 2012, Gu et al. proposed a method for reconstructing the interference-plus-noise covariance matrix based on curve integration. Although this method can better eliminate the desired signal component in the sampling covariance matrix, it is not accurate enough for the angle estimation of the interference signal. , and the integral operation of a large area causes a large time calculation complexity, which makes the application range of this method in practical engineering limited

Images