基于提升Keystone變換的聲吶寬帶自適應波束形成方法
doi: 10.11999/JEIT180394
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1.
南京電子技術研究所 ??南京 ??210039
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2.
中國電子科技集團公司智能感知技術重點實驗室 ??南京 ??210039
Sonar Broadband Adaptive Beamforming Based on Enhanced Keystone Transform
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1.
Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
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2.
Key Laboratory of IntelliSense Technology, CETC, Nanjing 210039, China
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摘要:
針對Keystone變換在寬帶陣列預處理方面的優(yōu)勢和常規(guī)Keystone變換存在的陣元數(shù)據(jù)缺失問題，該文將自回歸模型與常規(guī)Keystone變換相結合，提出一種基于提升Keystone變換的聲吶寬帶自適應波束形成算法。該算法首先將常規(guī)Keystone變換應用于寬帶陣列信號的相位對齊，接著采用自回歸模型對變換后各頻段缺失的陣元數(shù)據(jù)進行預測補償，最后通過穩(wěn)健自適應波束形成處理獲得目標方位輸出結果。仿真實驗結果表明，基于提升Keystone變換的寬帶自適應波束形成算法性能優(yōu)于常規(guī)Keystone自適應算法、指向最小方差自適應算法和聚焦自適應算法。
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關鍵詞:
- 寬帶自適應波束形成 /
- Keystone變換 /
- 自回歸模型
Abstract:Keystone transform is an effective broadband array signal pre-processing method, but it has a main problem of array data missing. In order to solve this problem, an enhanced Keystone transform algorithm, which combines the autoregression model with traditional Keystone transform, is proposed in this paper for sonar broadband adaptive beamforming. After phase alignment of broadband array signal using traditional Keystone transform, autoregression models for each frequency are constructed to compensate the missing array data. Then, a robust adaptive beamforming approach is utilized to obtain the target bearing results. The results of simulation studies indicate that the proposed broadband adaptive beamforming algorithm based on enhanced Keystone transform outperforms the beamforming algorithms based on traditional Keystone transform, steered minimum variance and frequency focusing.
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Key words:
- Broadband adaptive beamforming /
- Keystone transform /
- Autoregression model
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表 1 計算時間比較(s)
提升KS-RCB STMV-RCB 聚焦-RCB 64元，500 Hz帶寬 1.89 6.69 1.53 64元，750 Hz帶寬 2.27 8.86 1.93 128元，500 Hz帶寬 4.60 15.73 4.82 256元，500 Hz帶寬 15.39 61.41 22.17 
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