Signal fusion-based target detection algorithm for spatial diversity radar

Signal fusion-based radar target detection algorithms are studied in this article for spatial diversity multiple input multiple output (MIMO) radar based on the generalised likelihood ratio test (GLRT) algorithm, under the assumption that covariance matrices of clutter signals received by different...

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Bibliographic Details
Published in:IET radar, sonar & navigation Vol. 5; no. 3; pp. 204 - 214
Main Authors: Zhou, S.H., Liu, H.W.
Format: Journal Article
Language:English
Published: Stevenage Institution of Engineering and Technology 01.03.2011
The Institution of Engineering & Technology
Subjects:
Algorithms
Applied sciences
Clutter
Computer simulation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information theory
Information, signal and communications theory
Likelihood ratio
Navigation
Radar
Radiolocalization and radionavigation
Signal and communications theory
Signal, noise
Sonar
Target detection
Telecommunications
Telecommunications and information theory
Performance evaluation
Single frequency network
MIMO system
Space diversity
Algorithm
Clutter
Radar remote sensing
Radar target
Covariance
Simulation
Likelihood ratio test
Information processing
Radar
Data fusion
Target detection
ISSN:1751-8784, 1751-8792
Online Access:Get full text
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Summary:Signal fusion-based radar target detection algorithms are studied in this article for spatial diversity multiple input multiple output (MIMO) radar based on the generalised likelihood ratio test (GLRT) algorithm, under the assumption that covariance matrices of clutter signals received by different radar sites are different. Two signal fusion-based target detection algorithms are proposed, which can be applied in signal fusion networks (SFNs) with typical structures, at a low communication and computation cost. Simulation experiments in several scenarios indicate that two proposed signal fusion algorithms have better detection performances than decision fusion algorithms.
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ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2010.0100