NDLI: Maximum eigenvalue-based target detection for the K-distributed clutter environment

Content Provider	IET Digital Library
Author	Zhao, Wenjing Liu, Chang Liu, Wenlong Jin, Minglu
Abstract	Information geometry-based matrix constant false alarm rate (CFAR) detector is an efficient solution to target detection, especially for the K-distributed clutter environment with respect to a small bunch of pulses. The main reason for the matrix CFAR detector to achieve better detection performance is covariance matrix captures the correlations between the data. However, most existing matrix CFAR detectors suffer from heavy computational complexity, which leads to a limitation in practical detection scenarios. Motivated by this, the authors utilise the eigenvalues of the covariance matrix to capture the correlation and propose an eigenvalue-based detection method with lower computational complexity. Based on the Neyman–Pearson criterion, they first analyse the likelihood ratio test (LRT) in the eigenvalue domain and derive the relationship between the LRT test statistic and the maximum eigenvalue. To meet the practical requirement, they further design a totally blind scheme: the maximum eigenvalue-based matrix CFAR detector. By employing the group invariant theory, they show that the proposed detector presents the CFAR property. In addition, the theoretical performance analysis is also provided. Simulation results based on the numerical experiment and real sea clutter data verify that the proposed method with a low computational complexity can achieve a better detection performance.
Starting Page	1294
Ending Page	1306
Page Count	13
ISSN	17518784
Volume Number	12
e-ISSN	17518792
Issue Number	Issue 11, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5229
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computational Complexity Covariance Matrices Covariance Matrix Eigen Values And Eigen Function Geometry Information Geometry-based Matrix Constant False Alarm Rate Detector K-distributed Clutter Environment Likelihood Ratio Test Linear Algebra Low Computational Complexity LRT Test Statistic Maximum Eigenvalue-based Matrix CFAR Detector Maximum Eigenvalue-based Target Detection Neyman–Pearson Criterion Numerical Analysis Object Detection Probability Radar Clutter Radar Detection Radar Equipment Sea Clutter Data Signal Detection Statistics System And Application
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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