Open AccessASCRL evaluation with parametrically constrained covariance matrix
Author(s) -
Tan Jie,
Wang Yikai,
He Zishu,
Sun Guohao
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.0423
Subject(s) - clutter , covariance matrix , algorithm , trace (psycholinguistics) , gaussian , computer science , covariance , adaptive filter , filter (signal processing) , radar , matrix (chemical analysis) , mathematics , mathematical optimization , statistics , telecommunications , computer vision , physics , linguistics , philosophy , materials science , quantum mechanics , composite material
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.