Direction measurement method applicable to phase interference signal source under non stabilized noise background

Abstract

This invention relates to a method for testing directions of coherent signal sources under non-stable noise background including: collecting data received by channels and storing them in an EMS memory of the system, making adaptive uniform process to data received by the channels to form an array covariance matrix, utilizing a forward space smooth technology to preprocess it smoothly to constitute a forward space smooth covariance matrix and utilizing a forward and backward space smooth technology to smoothly pre-process the matrix to constitute a generalized covariance matrix based on the covariance matrix and a conjugated matrix and carrying out spectrum estimation to the generalized matrix with a sub-space class algorithm to identify a real incident direction of image false peak and signal source and output the real direction of the estimated signal source.

Description

technical field [0001] The invention belongs to the field of radar and communication, and relates to a spatial spectrum estimation signal processing method in the field. Specifically, the invention relates to a direction finding method suitable for coherent information sources under the background of non-stationary noise. Background technique [0002] Spatial spectrum estimation is widely used in radar, sonar, communication, seismic exploration and other fields, and it is a key technology to improve the performance of parameter estimation. However, conventional spatial spectrum estimation algorithms (such as MUSIC, ESPRIT, ML, WSF, etc.) mostly assume that the array noise is Gaussian white noise or stationary correlated colored noise. However, in the actual environment, the noise is often unknown or time-varying, and the non-stationary noise with unknown statistical properties (the noise covariance matrix is ​​a diagonal matrix of unknown unequal elements) often appears. Fo...

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Problems solved by technology

However, conventional spatial spectrum estimation algorithms (such as MUSIC, ESPRIT, ML, WSF, etc.) mostly assume that the array noise is Gaussian white noise or stationary correlated colored noise

However, in the actual environment, the noise is often unknown or time-varying, and non-stationary noise with unknown statistical properties (the noise covariance matrix is ​​a diagonal matrix of unknown unequal elements) often appears

For example, the inconsistency of the gain of each channel of the array element and the inconsistency of the internal noise of each channel will cause the output noise power of each channel to be different

In addition, for broadband signals, due to the influence of colored noise, such as cosmic noise or atmospheric noise, it will also cause the noise to be unstable in space and time

Due to the existence of non-stationary noise, the eigenvalues ​​of the noise after the eigendecomposition of the array covariance matrix are greatly disturbed, which will affect the structure of the noise subspace and the signal subspace, so that the subspace composed of the signal direction vector and the covariance After the matrix eigendecomposition, the subspace formed by the eigenvectors corresponding to the minimum eigenvalues ​​is not completely orthogonal, resulting in a large spatial spectrum estimation error

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