Jointing Time-frequency Distribution and Compressed Sensing for Countering Smeared Spectrum Jamming

LU Yunlong; LI Ming; CAO Runqing; WANG Zeyu; CHEN Hongmeng

doi:10.11999/JEIT160919

Volume 38 Issue 12

Jan. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(12): 3275-3281

LU Yunlong, LI Ming, CAO Runqing, WANG Zeyu, CHEN Hongmeng. Jointing Time-frequency Distribution and Compressed Sensing for Countering Smeared Spectrum Jamming[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3275-3281. doi: 10.11999/JEIT160919

Citation:

LU Yunlong, LI Ming, CAO Runqing, WANG Zeyu, CHEN Hongmeng. Jointing Time-frequency Distribution and Compressed Sensing for Countering Smeared Spectrum Jamming[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3275-3281. doi: 10.11999/JEIT160919

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Jointing Time-frequency Distribution and Compressed Sensing for Countering Smeared Spectrum Jamming

doi: 10.11999/JEIT160919

Funds:

The National Natural Science Foundation of China (61271297, 61272281, 61301284), The Specialized Research Fund for the Doctoral Program of Higher Education (20110203 110001)

Received Date: 2016-09-12
Rev Recd Date: 2016-11-25
Publish Date: 2016-12-19

Abstract

Abstract

The SMeared SPectrum (SMSP) jamming is a special active deception jamming for countering a Linear Frequency Modulated (LFM) pulse compression radar system. For suppressing the SMSP jamming, a novel anti-jamming approach is proposed based on jointing Time-Frequency (TF) distribution and Compressed Sensing (CS). First the SMSP jamming is isolated in the TF domain, and then the TF points belonging to target signal are reserved. According to the linear relationship between the reserved TF points and the frequency domain of radar echo, a CS minimization model is established and the Orthogonal Matching Pursuit (OMP) algorithm is utilized to recover the target signal, which means the SMSP jamming is mitigated simultaneously. The validity of the proposed method is evaluated using experimental data via Monte Carlo simulation.
- Active deception jamming,
- Jamming suppression,
- Time-frequency analysis,
- Compressed sensing

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References(28)

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