Signal arrival direction estimation method based on sparse processing

Abstract

Provided is a signal arrival direction estimation method based on sparse processing. The method comprises the following steps: a data autocorrelation matrix is estimated through an M-time snapshot data matrix receiving an array and an observation data vector is reconstructed through the data autocorrelation matrix; a measuring matrix and a corresponding sparse signal vector are constructed; a rough estimated value of the signal vector is calculated by utilization of a minimum absolute contraction and selection algorithm, an accurate estimated value of the signal vector is calculated by utilization of a weighing minimum absolute contraction and selection algorithm, an accurate estimated value of a signal arrival angle is obtained according to index of nonzero elements in the accurate estimated value of the signal vector, division by the position information and the space angle of the nonzero elements in the accurate estimated value of the signal vector is carried out, an arrival angle estimated value of the signal is obtained, and accurate estimation of the signal arrival angle is achieved. The method can achieve super-resolution arrival direction estimation in a low signal to noise ratio, and has high spatial resolution and estimation accuracy.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to a method for estimating the arrival angle of an array signal. Background technique [0002] Traditional subspace algorithms, such as MUSIC algorithm and ESPRIT algorithm, have excellent anti-noise effect and high parameter estimation accuracy. These algorithms divide the entire space into signal subspace and noise by eigendecomposition of the received data covariance matrix However, such calculation methods usually require a large amount of sampling data to ensure the estimation accuracy of the algorithm, so these algorithms are not suitable for high sampling cost or small number of sampling samples. Although the parameter estimation performance can be improved by increasing the number of array elements, this method not only increases the amount of received data, but also brings huge pressure to data transmission, storage, and processing, and makes hardware implementati...

AI Technical Summary

Problems solved by technology

[0002] Traditional subspace algorithms, such as MUSIC algorithm and ESPRIT algorithm, have excellent anti-noise effect and high parameter estimation accuracy. These algorithms divide the entire space into signal subspace and noise by eigendecomposition of the received data covariance matrix Subspace to estimate the angle of arrival (DOA) of the signal, but such calculation methods usually require a large amount of sampled data to ensure the estimation accuracy of the algorithm, so these algorithms are not suitable for high sampling costs or small number of sampling samples

Although the parameter estimation performance can be improved by increasing the number of array elements, this method not only increases the amount of received data, but also brings huge pressure to data transmission, storage, and processing, and makes hardware implementation more difficult. Suitable for engineering use

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