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GLRT detector based on knowledge aided covariance estimation in compound Gaussian environment
- Source :
- Signal Processing. 155:377-383
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- In order to alleviate the effect of the limited secondary data in the non-Gaussian clutter, a knowledge aided adaptive detector is proposed. The covariance matrix estimation is modeled as a general linear combination of prior covariance matrix and sample covariance matrix. Within this consideration, we obtain an adaptive detector based on the generalized likelihood ratio test. Experimental results on simulation and real data demonstrate that the proposed detector achieves better performance than the existing one-step GLRT (1S-GLRT) detectors when the secondary data are insufficient.
- Subjects :
- Physics::Instrumentation and Detectors
Computer science
Covariance matrix
Gaussian
Detector
020206 networking & telecommunications
02 engineering and technology
Sample mean and sample covariance
symbols.namesake
Estimation of covariance matrices
Control and Systems Engineering
Likelihood-ratio test
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
symbols
Clutter
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Algorithm
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 155
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi...........1e9b03a405be65910662ba691c1ac397