High-Accuracy DOA Estimation Algorithm at Low SNR Through Exploiting a Supervised Index

• Published in: IEEE transactions on aerospace and electronic systems Vol. 58; no. 4; pp. 3658 - 3665
• Main Authors: Xu, Kaijie, Xing, Mengdao, Zhang, Rui, E, Hanyu, Sha, Minghui, Nie, Weike, Quan, Yinghui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Array signal processing; Arrays; Breakdown; Closed loops; Correlation; Correlation analysis; Cost function; Direction of arrival; direction of arrival (DOA); Direction-of-arrival estimation; Eigenvalues; Eigenvalues and eigenfunctions; Estimation; Indexes; Parameter modification; root-mean-square error (RMSE); Signal processing; signal subspace; Signal to noise ratio; Similarity; Subspaces; Transformations (mathematics)
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2022.3144121

Performance of direction of arrival (DOA) estimation is one of the most important issues in array signal processing. Subspace-based algorithms provide a good compromise between the estimation accuracy and computational complexity. However, these methods are exposed to performance breakdown at low SNR scenarios. A major reason for such performance breakdown is the subspace swap phenomenon (intersubspace leakage). In this article, we elaborate on a novel modified signal subspace model through exploiting a supervised index for the estimation of DOA. With the developed model we refine the signal subspace so as to enhance the performance of the DOA estimation. In the proposed scheme, we define a fuzzy similarity matrix for the eigenvalues of the array output correlation matrix to capture the distribution of the eigenvalues. Then, we build up a transformation matrix between the fuzzy similarity matrix and the eigenspace of the correlation matrix, and construct a nonlinear transformation function to adjust the fuzzy similarity matrix. Subsequently, we define a supervised evaluation index named signal subspace reconstruction error for DOA estimation and construct a cost function of the signal subspace to develop a supervised model for the signal subspace. The signal subspace can be modified through adjusting the parameter in the nonlinear transformation function and optimizing the abovementioned cost function. Finally, the performance of DOA estimation can be enhanced with the modified signal subspace. The main characteristic of the proposed model is circularly applied feedback of the estimated DOA for refining the estimated subspace. It is a closed loop and supervised method not reported before. This article opens a specific way for improving the performance of the DOA estimation in array signal processing by a supervised index. However, the proposed method is still unsatisfying in some scopes of signal-to-noise ratio. We believe that exploiting a validity index for DOA estimation in array signal processing is still a general and interesting problem.