Sonar Broadband Adaptive Beamforming Based on Enhanced Keystone Transform
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摘要:
针对Keystone变换在宽带阵列预处理方面的优势和常规Keystone变换存在的阵元数据缺失问题，该文将自回归模型与常规Keystone变换相结合，提出一种基于提升Keystone变换的声呐宽带自适应波束形成算法。该算法首先将常规Keystone变换应用于宽带阵列信号的相位对齐，接着采用自回归模型对变换后各频段缺失的阵元数据进行预测补偿，最后通过稳健自适应波束形成处理获得目标方位输出结果。仿真实验结果表明，基于提升Keystone变换的宽带自适应波束形成算法性能优于常规Keystone自适应算法、指向最小方差自适应算法和聚焦自适应算法。
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关键词:
- 宽带自适应波束形成 /
- Keystone变换 /
- 自回归模型
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 计算时间比较(s)
提升KS-RCB STMV-RCB 聚焦-RCB 64元，500 Hz带宽 1.89 6.69 1.53 64元，750 Hz带宽 2.27 8.86 1.93 128元，500 Hz带宽 4.60 15.73 4.82 256元，500 Hz带宽 15.39 61.41 22.17 
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