High-Performance of Eigenvalue Decomposition on FPGA for the DOA Estimation

作     者：Zhang, Xiao-Wei Yan, Di Zuo, Lei Li, Ming Guo, Jian-Xin 

作者机构：Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Peoples R China Xijing Univ Sch Informat Engn Xian 710123 Peoples R China Changan Univ Sch Informat Engn Xian 710064 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 (IEEE Trans. Veh. Technol.)

年 卷 期：2023年第72卷第5期

页      面：5782-5797页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0823[工学-交通运输工程] 

基　　金：National Natural Science Foundation of China Aviation Science Fund [20170181001, 20172081010] 

主　　题：Matrix decomposition Jacobian matrices Field programmable gate arrays Eigenvalues and eigenfunctions Hardware Signal processing algorithms Multiple signal classification Eigenvalue decomposition (EVD) field-programmable gate array (FPGA) MUSIC Jacobi's method matrix inversion partial sorter QR algorithm 

摘      要：For the direction of arrival (DOA) in array signal processing, eigenvalue decomposition (EVD) is one key issue in hardware implementation of the multiple signal classification (MUSIC) algorithm. Therefore, we introduce the look-ahead simplified one-sided Jacobi s method to efficiently decompose those symmetric matrices in this article and prove that the new method has the best orthogonality of eigenvector and locates eigenvectors closest to the true solution in theory. Both the numerical performance and real-time are important in engineering, so we present the novel flexible hardware architecture in single floating point arithmetic for EVD on field-programmable gate arrays (FPGAs). Finally, the simulated and raw data are used to investigate the performance of some different approaches in the context of both the EVD and MUSIC algorithm. The experimental results show that our proposed method has the best performance.