Co-primer array non-grid DOA estimation method under non-negative sparse Bayes learning framework

Abstract

The invention provides a co-primer array non-grid DOA estimation method under a non-negative sparse Bayes learning framework, and belongs to the field of research on a high-resolution direction finding method in signal processing. The method includes the steps that firstly, a co-primer array received data covariance matrix is vectorized, a virtual received signal model is established, then a non-negative sparse Bayes model is established based on the characteristic that virtual incident signal elements in the model are not negative, hyper-parameters and a grid point set are iteratively updatedthrough an expectation-maximization algorithm, finally, a signal power spectrum is established according to the finally-updated grid point set and the finally-updated hyper-parameters, and then an estimated DOA is determined through spectrum peak searching. By means of the method, the operation process is converted to a real number field from a complex number field, and therefore the computationcomplexity can be reduced to a certain degree. In addition, by the application of a co-primer array, undetermined DOA estimation can be achieved, the limitation of the number of array elements in themaximum estimable information source number is broken through, thus, the hardware cost can be reduced to a certain degree, and certain engineering application value is achieved.

Description

technical field [0001] The invention belongs to the research field of high-resolution direction-finding methods in signal processing, and in particular relates to a non-grid DOA estimation method of a coprime array under a non-negative sparse Bayesian learning framework. Background technique [0002] A coprime array is a sparse array whose element spacing can be larger than half the wavelength of the signal. This characteristic makes it have a larger array aperture than a uniform array with the same number of elements, and thus has better DOA estimation performance. In addition, the vectorization operation of the covariance matrix of the received data of the coprime array can expand the aperture of the virtual array and increase the degree of freedom. It can not only realize overdetermined DOA estimation, but also underdetermined DOA estimation. For example, O(M+N) array elements can estimate at most O(MN) sources, while a uniform array with O(M+N) array elements can only es...

AI Technical Summary

Problems solved by technology

At the same time, in order to solve the problem of grid mismatch, the present invention proposes a new grid update method to gradually reduce the deviation between the real DOA of the incident signal and the preset grid points

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