中文

Journal of Applied Sciences ›› 2013, Vol. 31 ›› Issue (6): 585-592.doi: 10.3969/j.issn.0255-8297.2013.06.006

• Signal and Information Processing • Previous Articles     Next Articles

Generalized Approximate Maximum Likelihood Estimation of Covariance Matrix Structure

GU Xin-feng1,2, JIAN Tao1, HE You1, HAO Xiao-lin3   

  1. 1. Research Institute of Information Fusion, Naval Aeronautical and Astronautical
    University, Yantai 264001, Shandong Province, China
    2. China Satellite Martime Tracking and Control Department, Jiangyin 214431, Jiangsu Province，China
    3. Yantai Electricity and Economy Technical Institute, Yantai 264001, Shandong Province, China  
  • Received:2011-12-12 Revised:2012-02-16 Online:2013-11-29 Published:2012-02-16

PDF

1349

Cited

1

Abstract: By generalizing the clutter-clustered estimation method and considering the normalized sample covariance matrix (NSCM), a generalized NSCM(GNSCM) is proposed for covariance matrix structure estimation in correlated compound-Gaussian clutter. A maximum likelihood recursive estimation process of covariance matrix structure is derived in generalized clutter-clustered background. A generalized approximate maximum likelihood (GAML) estimator is then obtained by using GNSCM as the initialized estimation estimated matrix to recursive. GAML is an extension of the existing methods the approximate maximum likelihood (AML) and the constrained recursive clutter-clustered estimator (CRCCE). Simulation results show that, compared with
the two previous methods, GAML has higher estimation accuracy, and the corresponding adaptive detector has better constant false alarm ratio (CFAR) property and detection performance.

Key words:  non-Gaussian clutter, clutter-clustered, covariance matrix estimation, normalized matched filter, constant false alarm ratio

CLC Number: