Coprime array wave arrival direction angle estimation method based on variable-decibel bayesian inference

Abstract

The invention provides a coprime array wave arrival direction angle estimation method based on variable-decibel bayesian inference; receiving and sampling are performed on spatial signals by utilizingthe coprime array, and a sparse model vector is constructed according to the sampling covariance matrix; the wave arrival angle estimation problem is converted into a sparse reconstruction problem ina linear model, and an amplitude spectrogram is drawn; and an x-axis coordinate corresponding to the peak point is searched from the amplitude spectrogram, namely, the obtained wave arrival directionangle estimation value. The angle searching or angle matching process of the traditional method is avoided, meanwhile, the sampling frequency can be lower than the nyquist sampling rate, and better estimation performance can be obtained under the condition of a low snapshot number, so that the model dimension is greatly reduced, the calculation amount and the sparse reconstitution speed are improved, direct solution in the complex distribution form is avoided, the algorithm complexity is reduced, and the algorithm convergence speed is improved.

Description

technical field [0001] The invention relates to the field of signal processing, in particular to a method for estimating the direction-of-arrival angle of coprime array waves. Background technique [0002] Signal DOA estimation technology is an important content in the field of array signal processing. It refers to the use of sensor arrays to receive space signals, and then use modern signal processing methods to quickly and accurately estimate the incident direction of signal sources. It has been used in radar , sonar, wireless communication and many other fields have been widely used. [0003] The array structure mainly used in the traditional DOA estimation method is a uniform linear array, and the number of resolvable targets is limited by the number of physical elements of the array. Sparse arrays can obtain larger apertures than traditional uniform linear arrays when the number of array elements is the same, which reduces economic costs, and thus has attracted widespr...

AI Technical Summary

Problems solved by technology

This algorithm usually uses the expectation-maximum EM algorithm to iteratively solve the target parameters, but the EM algorithm is not suitable for the situation where the calculation of the prior distribution is difficult. The initial value of the parameter affects the local maximum obtained by iteration, and has high computational complexity and slow convergence speed. slow etc.

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