Electromagnetic vector sensor array space rotation decoherence direction finding method

Abstract

The invention relates to an electromagnetic vector sensor array space rotation decoherence direction finding method. An electromagnetic vector sensor linear array is randomly distributed on an x axis to receive K coherent narrowband steady far-field electromagnetic wave signals, and N-order synchronization sampling data is acquired by the array; self orthogonal vector characteristics of the electromagnetic vector sensor array are utilized, electromagnetic vector sensor array data is divided into 6 subarray data, according to subarray space rotation invariable characteristics, a rank of a data covariance matrix is recovered through space rotation decoherence processing to acquire the data covariance matrix after decoherence; a noise subspace is acquired through the data covariance matrix after decoherence, a MUSIC space spectrum is constructed through utilizing the noise subspace, and arrival angle estimation is realized through one-dimensional spectrum peak search of an angle domain. The method is advantaged in that the space rotation decoherence method has no array aperture loss and has a higher spatial resolution compared with a space smooth decoherence method, limit that the space smooth decoherence method is only applicable to uniform arrays is broken, and the space rotation decoherence method is applicable to uniform and non-uniform arrays.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a coherent source direction finding method of an electromagnetic vector sensor array. Background technique [0002] With the continuous acceleration of the informatization process, the electromagnetic environment is becoming more and more complex. In practice, due to the multipath phenomenon and echo signals in the signal transmission process and the electromagnetic interference released by humans, there are often more than one signal in the actual environment. When multiple When signals exist at the same time, these signals may be independent of each other, but it is more common for these signals to be correlated or coherent, so detection and estimation based on correlation sources is a very important research topic in signal processing. When the incident signals are independent of each other, the subspace method has good parameter estimation performance, b...

AI Technical Summary

Problems solved by technology

Although these methods solve the coherence problem of the signal, there are still some shortcomings. The computational complexity of the non-dimension reduction algorithm represented by focusing processing is greatly increased.

The dimension reduction method represented by spa

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