JOURNAL OF RADARS
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JOURNAL OF RADARS  2018, Vol. 7 Issue (4): 498-506    DOI: 10.12000/JR18002
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Waveform Design for Cognitive Radar Under Low PAR Constraints by Convex Optimization
Hao Tianduo①*   Cui Chen   Gong Yang  Sun Congyi
(Institute of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, China)
(Unit 96630, Beijing 102206, China
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Abstract To improve the detection performance of the radar transmit waveform while enabling the transmitter to perform at its maximal efficiency, a joint design method is proposed for the transmit and receive filter in the presence of signal-dependent clutter with a Peak-to-Average-power Ratio (PAR) constraint of the transmit waveform. First, an optimized model of the radar's output Signal-to-Interference-plus-Noise Ratio (SINR) for range-extended target detection is established. Second, the analytic expression of the receiver is obtained by converting the optimization problem into the Rayleigh quotient model. The optimal matrix solution is then obtained by transforming the non-convex problem into a convex problem via the semi-definite matrix of the waveform. Finally, the optimal vector solution of the waveform is extracted from the optimal matrix solution by combining the rank-one approximation method combined with the nearest neighbor method. An optimal waveform with a maximal output SINR for a given PAR range is obtained using the proposed method. The SINR value of the waveform when PAR=2 is the same as the SINR value of the optimized waveform under the energy constraint and is about 0.5 dB higher than the waveform when PAR=1. Simulation results demonstrate the effectiveness of the proposed method.
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Hao Tianduo
Cui Chen
Gong Yang
Sun Congyi
Key wordsCognitive radar   Waveform design   Peak-to-Average power Ratio (PAR)   Convex optimization   Semi-Definite Relaxation (SDR)     
Received: 2018-01-02; Published: 2018-06-04
Fund: The National Ministries Foundation
Cite this article:   
Hao Tianduo,Cui Chen,Gong Yang et al. Waveform Design for Cognitive Radar Under Low PAR Constraints by Convex Optimization[J]. JOURNAL OF RADARS, 2018, 7(4): 498-506.
 
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[1] Wang Lulu, Wang Hongqiang, Wang Manxi, Li Xiang. An Overview of Radar Waveform Optimization for Target Detection[J]. JOURNAL OF RADARS, 2016, 5(5): 487-498.
[2] Yang Jun, Zhang Qun, Luo Ying, Deng Donghu. Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing[J]. JOURNAL OF RADARS, 2016, 5(1): 90-98.
 

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