ASCRL evaluation with parametrically constrained covariance matrix. Issue 11 (2nd May 2018)

• Record Type: Journal Article
• Authors: Tan, Jie; Wang, Yikai; He, Zishu; Sun, Guohao
• Is Part Of: Electronics letters. Volume 54:Issue 11(2018)
• Page Start: 718
• Page End: 720
• Publication Date: 2018-05-02
• Subjects: adaptive filters -- covariance matrices -- radar clutter -- adaptive radar -- maximum likelihood estimation -- radar detection; two‐step strategy -- constrained Cramér–Rao -- CCM estimate -- adaptive filter -- ASCRL evaluation -- parametrically constrained covariance matrix -- average signal‐to‐clutter ratio loss -- adaptive radar filter -- parametrically constrained clutter covariance matrix; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0423
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : In this Letter, the authors propose an approach to evaluate the average signal‐to‐clutter ratio loss (ASCRL) of an adaptive radar filter with a parametrically constrained clutter covariance matrix (CCM). The approach is a two‐step strategy, the constrained Cramér–Rao bound of the CCM estimate is first derived, and then it is used to derive the ASCRL. More specifically, the ASCRL of an adaptive filter with a trace constrained CCM is derived. Simulation results show that our approach performs well in evaluating the trace constrained ASCRL for both complex Gaussian clutter and compound‐Gaussian clutter.