A Sparse Learning Based Detector with Enhanced Mismatched Signals Rejection Capabilities
ID:158 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:370 Oral Presentation

Start Time:2020-06-08 14:20(Asia/Shanghai)

Duration:20min

Session:S Special Session » SS07Advanced Techniques In Radar Detection, Localization, And Electronic Counter-Measures

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Abstract
This paper devises a detection architecture capable of rejecting mismatched signals embedded in Gaussian interference with unknown covariance matrix based on a sparse recovery technique. Specifically, a sparse learning method is exploited to estimate the amplitude and target angle of arrival, which are then employed to design detectors relying on the two-stage detection paradigm. Remarkably, the new decision scheme exhibits a bounded-constant false alarm rate property. The performance assessment, carried out by Monte Carlo simulations, shows that the new detectors can outperform the existing ones in terms of rejecting mismatched signals, while retaining reasonable detection performance for matched signals.
Keywords
Speaker
Sudan Han
National Innovation Institute of Defense Techonology, China

Submission Author
Sudan Han National Innovation Institute of Defense Techonology, China
Luca Pallotta University of Roma Tre, Italy
Gaetano Giunta University of Roma Tre, Italy
Wanli Ma National Innovation Institute of Defense Technology, China
Danilo Orlando Universita' degli Studi Niccolo' Cusano, Italy
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  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

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  • Dec 31 2020

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