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Extended Target Recognition in Cognitive Radar Networks

Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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Author to whom correspondence should be addressed.
Sensors 2010, 10(11), 10181-10197; https://doi.org/10.3390/s101110181
Received: 25 September 2010 / Revised: 30 October 2010 / Accepted: 1 November 2010 / Published: 11 November 2010
(This article belongs to the Section Remote Sensors)
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. View Full-Text
Keywords: cognitive radar network; radar waveform design; target recognition cognitive radar network; radar waveform design; target recognition
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MDPI and ACS Style

Wei, Y.; Meng, H.; Liu, Y.; Wang, X. Extended Target Recognition in Cognitive Radar Networks. Sensors 2010, 10, 10181-10197.

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