Fast FOCUSS method based on bi-conjugate gradient and its application to space-time clutter spectrum estimation

被引:5
作者
Bai, Gatai [1 ,3 ]
Tao, Ran [1 ,2 ,3 ]
Zhao, Juan [2 ,3 ]
Bai, Xia [2 ,3 ]
Wang, Yue [1 ,2 ,3 ]
机构
[1] Beijing Inst Technol, Sch Math & Stat, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[3] Beijing Key Lab Fract signal & Syst, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
focal underdetermined system solver (FOCUSS); sparse recovery (SR); bi-conjugate gradient (BICG); space-time adaptive processing (STAP); space-time clutter spectrum; MINIMUM NORM ALGORITHM; SPARSE REPRESENTATION; AIRBORNE RADAR; STAP METHOD; RECONSTRUCTION; DECOMPOSITION; SELECTION; RECOVERY; DOMAIN;
D O I
10.1007/s11432-015-1016-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The focal underdetermined system solver (FOCUSS) is a powerful tool for sparse representation in complex underdetermined systems. This paper presents the fast FOCUSS method based on the bi-conjugate gradient (BICG), termed BICG-FOCUSS, to speed up the convergence rate of the original FOCUSS. BICG-FOCUSS was specifically designed to reduce the computational complexity of FOCUSS by solving a complex linear equation using the BICG method according to the rank of the weight matrix in FOCUSS. Experimental results show that BICG-FOCUSS is more efficient in terms of computational time than FOCUSS without losing accuracy. Since FOCUSS is an efficient tool for estimating the space-time clutter spectrum in sparse recovery-based space-time adaptive processing (SR-STAP), we propose BICG-FOCUSS to achieve a fast estimation of the space-time clutter spectrum in mono-static array radar and in the mountaintop system. The high performance of the proposed BICG-FOCUSS in the application is demonstrated with both simulated and real data.
引用
收藏
页数:13
相关论文
共 32 条
  • [11] Joly P., 1993, Numerical Algorithms, V4, P379, DOI 10.1007/BF02145754
  • [12] Mihlyffy L., 1971, Linear Algebra Appl., V4, P95
  • [13] Reduced-rank STAP performance analysis
    Peckham, CD
    Haimovich, AM
    Ayoub, TF
    Goldstein, JS
    Reed, IS
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2000, 36 (02) : 664 - 676
  • [14] Peng Y, 2013, IEEE ANTENNAS PROP, P1406, DOI 10.1109/APS.2013.6711362
  • [15] Subset selection in noise based on diversity measure minimization
    Rao, BD
    Engan, K
    Cotter, SR
    Pahner, J
    Kreutz-Delgado, K
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2003, 51 (03) : 760 - 770
  • [16] An affine scaling methodology for best basis selection
    Rao, BD
    Kreutz-Delgado, K
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1999, 47 (01) : 187 - 200
  • [17] Saad Y., 1995, ITERATIVE METHODS SP
  • [18] Sparse Signal Estimation by Maximally Sparse Convex Optimization
    Selesnick, Ivan W.
    Bayram, Ilker
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (05) : 1078 - 1092
  • [19] Low-Rank Matrix Decomposition and Spatio-Temporal Sparse Recovery for STAP Radar
    Sen, Satyabrata
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2015, 9 (08) : 1510 - 1523
  • [20] Sun K, ARXIV10084185