Beam domain Root-MUSIC method based on covariance correction

Abstract

The invention discloses a beam domain Root-MUSIC method based on covariance correction. The method comprises the steps of building a uniform circular array far-field narrow-band signal model, and converting a spatial domain array output model to a beam domain in order to meet a needed Van der Monte-de structure; constructing a generalized linear combination covariance matrix, wherein the matrix isobtained by combining a traditional sample covariance matrix and a priori knowledge matrix; obtaining an initial DOA estimated value and a spatial domain steering vector by utilizing the covariance;solving a wave beam domain conversion error matrix caused by few sensors and an error matrix caused by subspace leakage under low snapshot; and searching an optimal correction factor to continuously reduce an error between the sample covariance and a true value, and finally obtaining a new direction of arrival by using the new covariance matrix. According to the method, the problem of subspace leakage under low snapshots and the beam domain conversion error caused by a small number of sensors are considered at the same time, so that the error between a sample covariance matrix and an ideal value can be remarkably reduced, and the estimation precision is improved.

Description

technical field [0001] The invention relates to a beam domain root-finding multiple signal classification (Root Multiple Signal Classification, Root-MUSIC) method based on covariance correction, which is applied to technical fields such as smart antennas, radars, and navigation. Background technique [0002] As one of the important research directions of array signal processing, the basic principle of space signal Direction of Arrival (DOA) estimation is to obtain discrete observation data of space signal source through sensor array, and process the received data to obtain the target orientation. . In recent years, many researchers have developed and perfected the DOA estimation method from various aspects, and widely used it in radar, sonar, seismic survey, speech processing system and other fields. Subspace algorithms are a class of classical algorithms for DOA estimation, mainly including MUSIC and rotation invariance (Estimation of Signal Parameter via Rotational Invari...

AI Technical Summary

Problems solved by technology

However, in practical applications, the real covariance matrix is ​​always difficult to obtain, and it is generally obtained by estimators such as forward average and forward and backward average. Therefore, the obtained sample covariance matrix is ​​often greatly affected by the number of snapshots, and the signal subspace and The noise subspace does not fully satisfy the completely orthogonal relationship, which will lead to the problem of subspace leakage

[0004] The commonly used array structures are one-dimensional linear array, two-dimensional planar array and three-dimensional solid array, among which the linear array is relatively simple and easy to realize, but this type of array can only realize the azimuth angle search of 180° of the source signal, and the signal space information provided is far away. not enough

Although the three-dimensional stereo array can collect all-round information of the spatial signal, the model processing is difficult and the calculation complexity is large

When the number of array elements is small, the error caused by the beam space conversion to the steering vector is often non-negligible, resulting in inaccurate estimation of the covariance matrix, which reduces the accuracy of DOA estimation

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