Degree-of-freedom enhanced spatial spectrum estimation method based on planar co-prime array block sampling tensor signal construction

Abstract

The invention discloses a degree-of-freedom enhanced spatial spectrum estimation method based on planar co-prime array block sampling tensor signal construction, and mainly solves the problems of signal multi-dimensional information loss and degree-of-freedom limitation in the existing method. The method comprises the following implementation steps: constructing a planar co-prime array; carrying out tensor modeling on sampling signals of the planar co-prime array block; deriving a virtual domain equivalent signal based on the cross-correlation statistics of the block sampling tensor signal; acquiring a block sampling equivalent received signal of the virtual domain uniform area array; constructing a three-dimensional block sampling virtual domain tensor signal and a four-order autocorrelation tensor thereof; constructing a signal and noise subspace based on virtual domain four-order autocorrelation tensor decomposition; and estimating a degree-of-freedom enhanced tensor spatial spectrum. According to the method, the planar co-prime array tensor signal is constructed based on a block sampling mode, the virtual domain equivalent tensor signal is derived, then the tensor spatial spectrum estimation with the enhanced degree of freedom is achieved through extraction of signal and noise subspace features in the four-order autocorrelation tensor, and the method can be used for passivedetection and positioning.

Description

Technical field [0001] The present invention belongs to the field of array signal processing technology, and in particular relates to a spatial spectrum estimation technology based on planar coprime array tensor signal modeling and statistical processing, in particular to a degree of freedom enhanced type based on the structure of planar coprime array block sampling tensor signal The spatial spectrum estimation method can be used for passive detection and positioning. Background technique [0002] As a two-dimensional sparse array with a systematic architecture, the planar coprime array has the characteristics of large aperture and high degree of freedom, and can realize high-precision and high-resolution spatial spectrum estimation; at the same time, by constructing a two-dimensional virtual domain and Processing based on second-order virtual domain statistics can effectively improve the degree of freedom of signal source spatial resolution. The traditional spatial spectrum est...

AI Technical Summary

Problems solved by technology

However, the existing method is still based on the actual received tensor signal processing, and does not use the two-dimensional virtual domain of the planar coprime array to construct the tensor space spectrum, and does not achieve the improvement of the degree of freedom performance

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