Soft sparse representation-based direction of arrival (DOA) estimation method

Abstract

The invention discloses a soft sparse representation-based direction of arrival (DOA) estimation method, and belongs to the technical field of radar signal processing. A soft sparse solution is calculated to estimate the orientation of a target source on the premise of sparsity by using an iterative weighted minimum variance method. The method comprises the following steps of first selecting an initial value and a regularization parameter, and determining an iteration finishing condition; then substituting the selected initial value and the selected parameter into a soft sparse representation iteration formula for iteration; and finally quitting iteration when consistency with the iteration finishing condition is achieved, obtaining the soft sparse solution, and determining the direction of an incoming wave, namely realizing the DOA estimation of a signal. According to the method, the shortcoming that a weak target can hardly be detected by the conventional DOA estimation method is overcome; a parameter selection strategy is simple, sensitivity to the selection of regularization parameter is avoided, and the method is wide in parameter selection range and high in adaptability particularly in case of no weak object; and the weak target can be detected, higher resolution is ensured, and the performance of the method is also higher than that of the conventional DOA estimation method.

Description

Technical field [0001] The present invention belongs to the technical field of radar signal processing, specifically: based on a new definition of sparsity-soft sparsity, an iterative weighted minimum variance method is applied to solve the sparsity solution, so as to estimate the orientation of the target signal source the goal of. Background technique [0002] The direction of arrival (DOA) estimation of a signal is an important research content in array signal processing, and it is widely used in many fields such as radar, wireless communication, electromagnetic field, sonar, seismic exploration and medical imaging. The main purpose of DOA estimation is to distinguish two targets that are very close in azimuth in a noisy environment. There are two types of commonly used DOA estimation methods, namely: non-parametric estimation methods and parameterized estimation methods. For non-parametric estimation methods, there are mainly beamforming (BF) method, multiple signal classif...

AI Technical Summary

Problems solved by technology

However, these methods may sometimes fail to detect weak objects that are somewhat stronger than noise (RWS)

However, in practical applications, weak targets are often encountered, for example, interference tends to be stronger than the target signal

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