Jian-Xin Guo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jian-Xin Guo has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Lei Zuo
National Key Laboratory of Radar Signal Processing, Xidian University, Xian, China
2 shared publications - Ming Li
National Key Laboratory of Radar Signal Processing, Xidian University, Xian, China
2 shared publications - Xiao-Wei Zhang
School of Information Engineering, Xijing University, Xi’an, China
2 shared publications - Di Yan
School of Information Engineering, Changan University, Xian, China
1 shared publication
Latest Publications
- 14 November 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Vehicular Technology
- Vol. 72 (5) , 5782-5797
High-Performance of Eigenvalue Decomposition on FPGA for the DOA Estimation
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 sim- plified 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 perform- ance 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.
- 14 November 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Vehicular Technology
- Vol. 72 (5) , 5782-5797
High-Performance of Eigenvalue Decomposition on FPGA for the DOA Estimation
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 sim- plified 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 perform- ance 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.
- 28 May 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 68 (7) , 6205-6216
High-Throughput FPGA Implementation of Matrix Inversion for Control Systems
In control engineering, numerical stablility and real-time are the two most important requirements for the matrix inversion. This paper presents an efficient and robust method for the field-programmable gate array (FPGA) calculation of the matrix inversion. We initially consider the scenario that the matrix to be processed is a non-singular Hermitian matrix. The proposed computation procedures are composed of the matrix decomposition, triangular matrix inversion and matrices multiplication. The first procedure is completed by LDL factorization based on the outer form of Cholesky'smethod, while the recursive algorithm for block sub-matrices is adopted to achieve the triangular matrix inversion. The new method has the high level in the parallel pipelining mechanism and steals the characteristics of both the upper triangular matrix and its inversion to reduce the computation load and improve the numerical stability. Meanwhile, the non-Hermitian matrix inversion can be easily solved if another procedure is added in the new method. Finally, we compare our method with the exiting FPGA-based techniques on one Xilinx Virtex-7 XC7VX690T FPGA. Furthermore, it has solved one array antenna control problem of the adaptive digital beam forming for one phased array radar successfully.
- 28 May 2020
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 68 (7) , 6205-6216
High-Throughput FPGA Implementation of Matrix Inversion for Control Systems
In control engineering, numerical stablility and real-time are the two most important requirements for the matrix inversion. This paper presents an efficient and robust method for the field-programmable gate array (FPGA) calculation of the matrix inversion. We initially consider the scenario that the matrix to be processed is a non-singular Hermitian matrix. The proposed computation procedures are composed of the matrix decomposition, triangular matrix inversion and matrices multiplication. The first procedure is completed by LDL factorization based on the outer form of Cholesky'smethod, while the recursive algorithm for block sub-matrices is adopted to achieve the triangular matrix inversion. The new method has the high level in the parallel pipelining mechanism and steals the characteristics of both the upper triangular matrix and its inversion to reduce the computation load and improve the numerical stability. Meanwhile, the non-Hermitian matrix inversion can be easily solved if another procedure is added in the new method. Finally, we compare our method with the exiting FPGA-based techniques on one Xilinx Virtex-7 XC7VX690T FPGA. Furthermore, it has solved one array antenna control problem of the adaptive digital beam forming for one phased array radar successfully.