NDLI: Clutter covariance matrix estimation using weight vectors in knowledge-aided STAP

Content Provider	IET Digital Library
Author	Jeon, H. Chung, Y. Chung, W. Kim, J. H. Yang, H.
Abstract	A knowledge-aided space–time adaptive processing (STAP) is a quite useful technique to suppress non-stationary and heterogeneous clutter. It estimates a covariance matrix by combining a conventional covariance matrix based on secondary data with a synthesised one by prior information. A new combining method is presented, where weight vectors, rather than constant weights, are used to combine two covariance matrices. In this method, the weight vectors are derived in a way to maximise clutter-to-noise ratio of the combined covariance matrix. A numerical simulation is conducted for a bistatic radar scenario where clutter non-stationarity and heterogeneity can be assumed and the performance of the proposed method is demonstrated in terms of clutter suppression and target detection.
Starting Page	560
Ending Page	562
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 8, Apr (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.4631
Journal	Electronics Letters
Publisher Date	2017-03-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bistatic Radar Clutter Covariance Matrix Estimation Clutter-to-noise Ratio Covariance Matrices Estimation Theory Heterogeneous Clutter Suppression Knowledge-aided Space-time Adaptive Processing Knowledge-aided STAP Linear Algebra Nonstationary Clutter Suppression Numerical Analysis Numerical Simulation Radar Clutter Radar Signal Processing Radar Theory Signal Processing And Detection Space-time Adaptive Processing Statistics Target Detection Weight Vector
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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