Direction of arrival estimation method for far-field coherent signals based on subspace and interpolation transformation

Abstract

The invention discloses a far-field coherent signal direction-of-arrival estimation method based on subspace and interpolation transformation, which specifically calculates the correlation vector on each sub-interval; calculates the optimal interpolation matrix; the interpolation matrix and the correlation vector of the real array, and solves the The correlation vector of the virtual matrix; then construct the correlation matrix of the real array and the virtual array; then construct the linear operator and the orthogonal projection operator; finally calculate the polynomial root, solve the estimation of the direction of arrival of the far-field coherent signal, and construct the linear array subspace , solve the correlation vector, and avoid the computationally complex eigendecomposition operation in the traditional subspace algorithm under the premise of ensuring accuracy; interpolate the non-uniform array into a uniform array, and then use the root-finding algorithm to estimate the direction of arrival, avoiding the complex calculation In addition, the invention extends the scope of application from uniform linear arrays to arbitrary linear arrays, and is still effective when the number of sensors is large or real-time estimation is required.

Description

technical field [0001] The invention belongs to the technical field of array signal processing, and in particular relates to a method for estimating the direction of arrival of far-field coherent signals based on subspace and interpolation transformation. Background technique [0002] Source location has important applications in the fields of radar, sonar, wireless communication, etc. In practical applications, due to multi-path propagation of signals or intelligent interference in military affairs, the received signals are often coherent. In recent decades, subspace-based DOA estimation algorithms represented by Multiple Signal Classification (MUSIC) have been favored due to their good estimation accuracy. However, these traditional subspace algorithms are all based on the eigendecomposition of the covariance matrix, which is computationally complex and difficult to meet the requirements of large-scale arrays or real-time estimation. In order to avoid the computationally ...

AI Technical Summary

Problems solved by technology

However, the ideal condition of a uniform linear array is difficult to satisfy in practical applications.

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