An Efficient FPGA Implementation of MUSIC Processor Using Cyclic Jacobi Method: LiDAR Applications

被引:2
|
作者
Ghayoula, Ridha [1 ,2 ]
Amara, Wided [3 ]
El Gmati, Issam [4 ]
Smida, Amor [2 ,5 ]
Fattahi, Jaouhar [1 ]
机构
[1] Laval Univ, Dept Elect & Comp Engn, Quebec City, PQ G1V 0A6, Canada
[2] Tunis El Manar Univ, Fac Math Phys & Nat Sci Tunis, Microwave Elect Res Lab, Tunis 2092, Tunisia
[3] Univ Tunis El Manar, SysCom Lab, ENIT, Tunis 1068, Tunisia
[4] Umm Al Qura Univ, Coll Engn, Mecca 24382, Saudi Arabia
[5] Majmaah Univ, Coll Appl Med Sci, Dept Med Equipment Technol, Almajmaah 11952, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 19期
关键词
FPGA; MUSIC processor; cyclic Jacobi method; eigenvector; eigenvalue; covariance matrix; HARDWARE IMPLEMENTATION; DOA ESTIMATOR; SPARSE;
D O I
10.3390/app12199726
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
LiDAR is a technology that uses lasers to measure the position of elements. Measuring the laser travel time and calculating the distance between the LiDAR and the surface requires the calculation of eigenvalues and eigenvectors of the convergence matrix. SVD algorithms have been proposed to solve an eigenvalue problem, which is computationally expensive. As embedded systems are resource-constrained hardware, optimized algorithms are needed. This is the subject of our paper. The first part of this paper presents the methodology and the internal architectures of the MUSIC processor using the Cyclic Jacobi method. The second part presents the results obtained at each step of the FPGA processing, such as the complex covariance matrix, the unitary and inverse transformation, and the value and vector decomposition. We compare them to their equivalents in the literature. Finally, simulations are performed to select the way that guarantees the best performance in terms of speed, accuracy and power consumption.
引用
收藏
页数:21
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