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基于知識輔助的MIMO雷達波形設計方法

關(guān)鍵,  李秀友,  黃勇,  薛永華

關(guān)鍵, 李秀友, 黃勇, 薛永華. 基于知識輔助的MIMO雷達波形設計方法[J]. 電子與信息學報, 2016, 38(12): 3063-3069. doi: 10.11999/JEIT160008
引用本文: 關(guān)鍵, 李秀友, 黃勇, 薛永華. 基于知識輔助的MIMO雷達波形設計方法[J]. 電子與信息學報, 2016, 38(12): 3063-3069. doi: 10.11999/JEIT160008
GUAN Jian, LI Xiuyou, HUANG Yong, XUE Yonghua. Knowledge-aided MIMO Radar Waveform Design Method[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3063-3069. doi: 10.11999/JEIT160008
Citation: GUAN Jian, LI Xiuyou, HUANG Yong, XUE Yonghua. Knowledge-aided MIMO Radar Waveform Design Method[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3063-3069. doi: 10.11999/JEIT160008

基于知識輔助的MIMO雷達波形設計方法

doi: 10.11999/JEIT160008
基金項目: 

國家自然科學基金(61471382, 61401495, 61201445, 61179017, 61501487)，山東省自然科學基金(2015ZRA06052)，泰山學者建設工程專項經(jīng)費

Knowledge-aided MIMO Radar Waveform Design Method

Funds: 

The National Natural Science Foundation of China (61471382, 61401495, 61201445, 61179017, 61501487), Natural Science Foundation of Shandong Province (2015ZRA 06052), Special Funds of Taishan Scholars Construction Engineering

  • 摘要: 該文針對雷達系統(tǒng)受到天線主瓣和副瓣雜波以及強干擾影響時性能下降問題，提出基于距離擴展目標和雜波先驗信息的MIMO雷達波形設計方法。首先建立了目標函數(shù)，綜合考慮了波束主瓣增益、旁瓣雜波抑制能力以及目標輸出SCNR的改善性能；然后在優(yōu)化問題求解中對約束條件進行松弛，使得波形矩陣空域和時域2維解耦合，從而實現(xiàn)空域波束形成和時域波形設計獨立優(yōu)化求解；其次利用L-BFGS算法設計恒模的發(fā)射波形矩陣，形成低副瓣的波束方向圖和較深的強雜波抑制凹口，并基于目標輸出SCNR最大化準則，利用迭代算法分步求解優(yōu)化的主瓣發(fā)射波形和接收濾波器；最后通過電磁仿真的距離擴展目標數(shù)據(jù)驗證所提算法的有效性。
    Abstract: In order to solve the problem of performance degradation when radar system is influenced by clutter from mainlobe and sidelobe, MIMO radar waveform design algorithm based on knowledge of range-spread target and clutter is investigated. Firstly, an optimization cost function is established, which includes mainlobe gain, sidelobe clutter suppression capability and Signal to Clutter plus Noise Ratio (SCNR) improvement. Secondly, to tackle the optimization problem, a relaxation is made to decouple spatial and temporal domain of the waveform matrix, beamforming and waveform design can be solved independently. Thirdly, L-BFGS algorithm is used to design the unimodular waveform matrix, beampattern with lower sidelobe and deep null is got. Based on maximization of SCNR, transmitted waveform and receiving filter are designed by iterative algorithm. Finally, the effectiveness of the proposed algorithm is verified by electromagnetic simulation of range-spread target.
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  • VESPE M, JONES G, and BAKER C J. Lessons for radar: waveform diversity in echolocating mammals[J]. IEEE Signal Processing Magazine, 2009, 26(1): 65-75.
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    HULEIHEL W, TABRIKIAN J, and SHAVIT R. Optimal adaptive waveform design for cognitive MIMO radar[J]. IEEE Transactions on Signal Processing, 2013, 61(20): 5075-5089.
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    WANG Yongchao, WANG Xu, LIU Hongwei, et al. On the design of constant modulus probing signals for MIMO radar[J]. IEEE Transactions on Signal Processing, 2012, 60(8): 4432-4438.
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  • 文章訪問數(shù):  1467
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  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2016-01-04
  • 修回日期:  2016-05-30
  • 刊出日期:  2016-12-19

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