Underdetermined broadband signal DOA estimation method based on sparse Bayesian in unknown noise field

Abstract

The invention discloses an underdetermined broadband signal DOA estimation method based on sparse Bayesian in an unknown noise field, and the method comprises the following steps: firstly introducinga co-prime array which employs a minimum sparse scale to reconstruct a space covariance matrix, and employing a non-uniform sampling method; vectorizing the covariance matrix, and obtaining a virtualmanifold matrix from the co-prime array by utilizing a Kronecker product; secondly, preprocessing the vectorized covariance matrix, preliminarily inhibiting unknown noise signals in the acquired signals, and weakening the interference of noise on target signal positioning; and finally, introducing a sparse Bayesian algorithm, applying the sparse Bayesian algorithm to a sparse signal recovery model, obtaining a posterior probability through a Bayesian rule, estimating all hyper-parameters, and updating the real DOA estimation of the target signal. According to the invention, the problem of non-convex optimization can be solved, the sparsity is automatically determined by utilizing fixed-point updating, and a better processing effect is achieved under the condition that a small number of samples are collected, especially under the condition of a low signal-to-noise ratio.

Description

technical field [0001] The invention belongs to the technical field of DOA estimation of microphone array signals, and in particular relates to an underdetermined broadband signal DOA estimation method based on sparse Bayesian in an unknown noise field. Background technique [0002] Estimation of broadband Direction of Arrival (DOA) using sensor arrays is an active research topic due to its wide range of applications requiring the estimation of the so-called angular spectrum, for example, in radar, sonar, wireless communication and positioning, etc. Since the DOA estimation accuracy is determined by the degree of freedom (DOF) of the sensor array, evenly spaced arrays need to increase the number of sensors to obtain a higher DOF, which increases the manufacturing cost and the difficulty of array calibration. Sparse arrays, namely nested arrays and coprime arrays, can achieve higher DOF numbers and resolve more sources than physical sensor numbers using non-uniform sensor loc...

AI Technical Summary

Problems solved by technology

Since the DOA estimation accuracy is determined by the degree of freedom (DOF) of the sensor array, evenly spaced arrays need to increase the number of sensors to obtain a higher DOF, which increases the manufacturing cost and the difficulty of array calibration

Images