Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 9; p. 2915
• Main Authors: Zhao, Yonghong, Zheng, Gang, Wang, Junlong, Liu, Jisong, Dong, Shuxin, Xin, Jing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.05.2025; MDPI
• Subjects: Accuracy; Algorithms; broadband signals; DOA estimation; Efficiency; genetic algorithm; Genetic algorithms; Machine learning; Methods; Musical performances; Neural networks; Radar; Signal processing; Spectrum allocation; support vector regression; DOA estimation; genetic algorithm; support vector regression; machine learning; broadband signals
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25092915

High-precision direction of arrival (DOA) of wideband signals is a very important technology in the field of radar and communication. In this work, we propose an efficient support vector regression (SVR) architecture via a genetic algorithm (GA) for wideband DOA estimation, which exhibits high estimation performance and generalization performance. By adopting the two-sided correlation transformation (TCT) algorithm, the network is trained only from reference frequency data to increase the training efficiency. In order to reduce the redundant information in the array covariance matrix and lower the dimensionality of the input features, the array covariance matrix at a single frequency point is preprocessed according to its conjugate symmetry and elemental characteristics, and the dimensionality-reduced input features are obtained. Specifically, the dimensionality of the input features does not increase with the number of sub-bands when dealing with broadband signals or ultra-broadband signals, which can significantly reduce the training time of the model and the storage capacity of the system. The increased performance of the proposed algorithm is highly desirable in resource-constrained scenarios, and the experimental results demonstrate the efficiency and superiority of the proposed network compared with existing methods.