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High-Accuracy DOA Estimation Based on an Improved Sample Correlation Matrix
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AI
DOI：10.1109/LGRS.2023.3259426.png)
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Abstract
En 中文
In a direction-finding process, high-resolution subspace-based algorithms are the most popular ones. It is well-known that their performance of direction of arrival (DOA) estimation mainly depends on the accuracy of the signal subspace. However, the traditional methods of capturing the signal subspace do not mine the information hidden in the array output in depth, which may restrict their application to some extent. In this study, we elaborate on a novel scheme to extract the signal subspace through refinement of the correlation matrix of the array output. In the developed scheme, a collection of spatial-temporal correlation matrices is first established. Then, we define a weighting vector for the correlation matrices and take the weighted average of the correlation matrices as the covariance matrix of the array output. It is clear that this covariance matrix is more general than the traditional covariance matrix, and the signal subspace can be optimized through adjustment of the weighting vector. In this study, we present an optimal weighting vector by adopting particle swarm optimization (PSO). Simulation results demonstrate that the proposed approach has a better performance in root mean square error (RMSE) compared to the existing schemes.
Keywords:
Correlation
Direction-of-arrival estimation
Covariance matrices
Estimation
Arrays
Eigenvalues and eigenfunctions
Particle swarm optimization
Array signal processing
direction of arrival (DOA)
particle swarm optimization (PSO)
root mean square error (RMSE)
weighted sample correlation matrix
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K