3D Co-Prime Cubic Array Direction-of-Arrival Estimation via Cross-Correlation Tensor
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Solution Overview
Problem
Existing methods for direction-of-arrival estimation in three-dimensional co-prime cubic arrays suffer from multi-dimensional signal structured information loss and Nyquist mismatch, particularly when expanding traditional vectorized signal processing to three-dimensional spaces.
Innovation Solution
A three-dimensional co-prime cubic array direction-of-arrival estimation method based on cross-correlation tensor statistics is developed, utilizing a four-dimensional tensor modeling and CANDECOMP/PARAFAC decomposition to construct a virtual domain signal tensor, effectively retaining multi-dimensional structured information and avoiding spatial smoothing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vectorized signal processing is expanded to three-dimensional co-prime cubic array scene, then direction-of-arrival estimation can be performed, but multi-dimensional spatial information structure is destroyed and aliasing occurs
Solution Approach 1:
The patent transitions from traditional vectorized signal processing to tensor-based signal processing, adding dimensional structure to represent the three-dimensional co-prime cubic array geometry. By using fourth-order tensors to model the receiving signal, the method preserves the multi-dimensional spatial information structure while enabling direction-of-arrival estimation, thereby resolving the contradiction between estimation capability and information preservation.
2Adaptability or versatility
If autocorrelation statistics-based calculation method is used, then virtual domain signal processing can be performed, but Nyquist matching requirement cannot be satisfied in three-dimensional co-prime cubic array scene
Solution Approach 1:
The patent changes the statistical parameter from autocorrelation to cross-correlation in the tensor model. By using cross-correlation tensor statistics instead of autocorrelation, the method achieves Nyquist matching for three-dimensional co-prime cubic arrays, resolving the contradiction between virtual domain processing adaptability and Nyquist matching precision.
Data Source
AI summary
The present disclosure discloses a three-dimensional co-prime cubic array direction-of-arrival estimation method based on a cross-correlation tensor, mainly solving the problems of multi-dimensional signal structured information loss and Nyquist mismatch in existing methods and comprising the following implementing steps: constructing a three-dimensional co-prime cubic array; carrying out tensor modeling on a receiving signal of the three-dimensional co-prime cubic array; calculating six-dimensional second-order cross-correlation tensor statistics; deducing a three-dimensional virtual uniform cubic array equivalent signal tensor based on cross-correlation tensor dimension merging transformation; constructing a four-dimensional virtual domain signal tensor based on mirror image augmentation of the three-dimensional virtual uniform cubic array; constructing a signal and noise subspace in a Kronecker product form through virtual domain signal tensor decomposition; and acquiring a direction-of-arrival estimation result based on three-dimensional spatial spectrum search.