Gradient Search Non-Orthogonal Approximate Joint Diagonalization Algorithm  

作　　者：李细林 张贤达 李培胜 

机构地区：[1]Department of Automation, Tsinghua University

出　　处：《Tsinghua Science and Technology》2007年第6期669-673,共5页清华大学学报（自然科学版（英文版）

基　　金：Supported by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList) ;the National Natural Science Foundation of China (No. 60675002)

摘　　要：The problem of approximate joint diagonalization of a set of matrices is instrumental in numerous statistical signal processing applications. This paper describes a relative gradient non-orthogonal approximate joint diagonalization （AJD） algorithm based on a non-least squares AJD criterion and a special AJD using a non-square diagonalizing matrix and an AJD method for ill-conditioned matrices. Simulation results demonstrate the better performance of the relative gradient AJD algorithm compared with the conventional least squares （LS） criteria based gradient-type AJD algorithms. The algorithm is attractive for practical applications since it is simple and efficient.The problem of approximate joint diagonalization of a set of matrices is instrumental in numerous statistical signal processing applications. This paper describes a relative gradient non-orthogonal approximate joint diagonalization （AJD） algorithm based on a non-least squares AJD criterion and a special AJD using a non-square diagonalizing matrix and an AJD method for ill-conditioned matrices. Simulation results demonstrate the better performance of the relative gradient AJD algorithm compared with the conventional least squares （LS） criteria based gradient-type AJD algorithms. The algorithm is attractive for practical applications since it is simple and efficient.

关 键 词：approximate joint diagonalization gradient methods DECORRELATION STATISTICS adaptive signa processing 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]