Errancy direction-of-arrival estimation method based on sparse Bayesian learning

Abstract

The invention discloses an errancy direction-of-arrival estimation method based on sparse Bayesian learning. The method comprises the steps that a sparse array composed of M array elements is constructed at the receiving end, and an array receiving signal model is constructed; a super complete dictionary based on the array guiding vector is constructed according to the compressed sensing theory, and the array receiving signal model is expanded to a sparse signal reconstructed model X; the sparse signal reconstructed model X is utilized for constructing a sparse signal reconstructed model Y corresponding to a virtual signal; values of designated parameters are initialized, and the noise power of an airspace signal in the transmitting process is determined; a virtual array output signal is calculated; the mean value and variance of a probability density function after outputting of the virtual signal are subjected to checking calculation; the power spectrum, noise power and quantizationerror of an output signal of the virtual signal are subjected to iterative calculation by utilizing Bayesian learning; a termination criterion is set; the wave shape of the power spectrum is drawn, peak values on the power spectrum are found, the result of estimating the direction-of-arrival is obtained based on the peak values; according to the method, the number of signals more than the array elements can be estimated, and the estimation accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to the direction of arrival estimation of radar signals, acoustic signals and electromagnetic signals. target detection. Background technique [0002] DOA (Direction-of-Arrival) estimation is an important branch in the field of array signal processing. It refers to the use of array antennas to receive airspace signals, and to process the received signals through statistical signal processing techniques and various optimization methods. In order to restore the direction information of the incident signal, it has a wide range of applications in the fields of radar, sonar, voice and wireless communication. [0003] Uniform linear array is the most commonly used type of array structure in existing DOA estimation methods, because it satisfies the Nyquist sampling theorem and can realize effective DOA estimation. However, the degree of freedom of the DOA estimation...

AI Technical Summary

Problems solved by technology

[0006] The main purpose of the present invention is to solve the problem of low accuracy of DOA estimation in the prior art, and to provide a method for estimating the direction of arrival of off-grid waves based on sparse Bayesian learning. This method uses the virtual array corresponding to the sparse array to have The fact of more degrees of freedom restores the signal received by the virtual array, uses the sparse Bayesian learning theory to realize the joint solution of the incident angle and quantization error, and makes full use of all the information contained in the sparse array received signal, specifically The technical solution is as follows:

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