非準(zhǔn)確先驗(yàn)知識(shí)下認(rèn)知雷達(dá)低峰均比穩(wěn)健波形設(shè)計(jì)

郝天鐸; 周青松; 孫從易; 崔琛

doi:10.11999/JEIT170560

非準(zhǔn)確先驗(yàn)知識(shí)下認(rèn)知雷達(dá)低峰均比穩(wěn)健波形設(shè)計(jì)

doi: 10.11999/JEIT170560

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出版歷程
- 收稿日期: 2017-06-09
- 修回日期: 2017-11-02
- 刊出日期: 2018-03-19

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

摘要

摘要: 針對(duì)目標(biāo)和雜波先驗(yàn)知識(shí)不準(zhǔn)確時(shí)認(rèn)知雷達(dá)的檢測(cè)波形設(shè)計(jì)問(wèn)題，同時(shí)兼顧功率放大器對(duì)低峰均比(PAR)波形的需求，該文提出一種信號(hào)相關(guān)雜波背景下認(rèn)知雷達(dá)低PAR穩(wěn)健波形設(shè)計(jì)方法。首先，在目標(biāo)和雜波不確定集范圍內(nèi)，基于極大極小化準(zhǔn)則構(gòu)造關(guān)于輸出信干噪比(SINR)的優(yōu)化模型；然后將不確定性參數(shù)代入該模型，給出最差SINR下對(duì)應(yīng)雜波協(xié)方差矩陣和目標(biāo)Toeplitz矩陣的取值；在此基礎(chǔ)上，利用半正定松弛，將非凸的優(yōu)化模型轉(zhuǎn)化為關(guān)于發(fā)射波形半正定矩陣的凸問(wèn)題進(jìn)行求解；最后，通過(guò)秩1近似法結(jié)合最近鄰方法，進(jìn)一步從波形的最優(yōu)矩陣解中提取出最優(yōu)向量解。分析表明，在穩(wěn)健性能相同的情況下，與現(xiàn)有方法相比該算法具有更低的運(yùn)算量，仿真結(jié)果驗(yàn)證了所提方法的有效性和穩(wěn)健性。
- 認(rèn)知雷達(dá) /
- 穩(wěn)健波形 /
- 低峰均比 /
- 凸優(yōu)化 /
- 半正定松弛
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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