Low-PAR Robust Waveform Design for Cognitive Radar with Imprecise Prior Knowledge

HAO Tianduo; ZHOU Qingsong; SUN Congyi; CUI Chen

doi:10.11999/JEIT170560

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 532-540

HAO Tianduo, ZHOU Qingsong, SUN Congyi, CUI Chen. Low-PAR Robust Waveform Design for Cognitive Radar with Imprecise Prior Knowledge[J]. Journal of Electronics & Information Technology, 2018, 40(3): 532-540. doi: 10.11999/JEIT170560

Citation:

HAO Tianduo, ZHOU Qingsong, SUN Congyi, CUI Chen. Low-PAR Robust Waveform Design for Cognitive Radar with Imprecise Prior Knowledge[J]. Journal of Electronics & Information Technology, 2018, 40(3): 532-540. doi: 10.11999/JEIT170560

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Low-PAR Robust Waveform Design for Cognitive Radar with Imprecise Prior Knowledge

doi: 10.11999/JEIT170560

Received Date: 2017-06-09
Rev Recd Date: 2017-11-02
Publish Date: 2018-03-19

Abstract

Abstract

In view of the detection waveform design for cognitive radar with imprecise prior knowledge of target and clutter, while considering the demand of power amplifier on low Peak-to-Average power Ratio (PAR) waveform, a low-PAR robust waveform design method in presence of signal-dependent clutter is proposed. Firstly, the optimization model of radars output Signal-to-Interference-plus-Noise Ratio (SINR) is established within the uncertainty of target and clutter via Max-Min method. Secondly, the clutter covariance matrix and Toeplitz matrix of target corresponding to worst-case SINR is obtained. Since the optimization problem of waveform is non-convex, Semi-Definite Relaxation (SDR) is adopted to converse the non-convex problem into a convex problem, which is about the semi-definite matrix of waveform. Finally, the optimal vector solution of waveform can be extracted from the optimal matrix solution by the rank-one approximation method combined with the nearest neighbor method. Compared with the existing methods, the computational complexity of the proposed method is obviously reduced without losing robust performance according to the analysis. The simulation results demonstrate the effectiveness and robustness of the proposed method.
- Cognitive radar,
- Robust waveform,
- Low Peak-to-Average power Ratio (PAR),
- Convex optimization,
- Semi-Definite Relaxation (SDR)

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References(25)

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