Extended Target Recognition in Cognitive Radar Networks

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypoth...

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Main Authors: Xiqin Wang, Yimin Liu, Huadong Meng, Yimin Wei
Format: Article
Language:English
Published: MDPI AG 2010-11-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/10/11/10181/
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author Xiqin Wang
Yimin Liu
Huadong Meng
Yimin Wei
author_facet Xiqin Wang
Yimin Liu
Huadong Meng
Yimin Wei
author_sort Xiqin Wang
collection DOAJ
description We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.
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spelling doaj.art-fba1620e1f4843558748151fa911675e2022-12-22T04:22:40ZengMDPI AGSensors1424-82202010-11-011011101811019710.3390/s101110181Extended Target Recognition in Cognitive Radar NetworksXiqin WangYimin LiuHuadong MengYimin WeiWe address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.http://www.mdpi.com/1424-8220/10/11/10181/cognitive radar networkradar waveform designtarget recognition
spellingShingle Xiqin Wang
Yimin Liu
Huadong Meng
Yimin Wei
Extended Target Recognition in Cognitive Radar Networks
Sensors
cognitive radar network
radar waveform design
target recognition
title Extended Target Recognition in Cognitive Radar Networks
title_full Extended Target Recognition in Cognitive Radar Networks
title_fullStr Extended Target Recognition in Cognitive Radar Networks
title_full_unstemmed Extended Target Recognition in Cognitive Radar Networks
title_short Extended Target Recognition in Cognitive Radar Networks
title_sort extended target recognition in cognitive radar networks
topic cognitive radar network
radar waveform design
target recognition
url http://www.mdpi.com/1424-8220/10/11/10181/
work_keys_str_mv AT xiqinwang extendedtargetrecognitionincognitiveradarnetworks
AT yiminliu extendedtargetrecognitionincognitiveradarnetworks
AT huadongmeng extendedtargetrecognitionincognitiveradarnetworks
AT yiminwei extendedtargetrecognitionincognitiveradarnetworks