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Broadband Aperture Extension of A Passive Sonar Array Using Multi-Dimension Autoregression Model

机译:多维自回归模型的无源声纳阵列宽带孔径扩展

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This paper presents a broadband aperture extension approach of a passive sonar array based on multi-dimension autoregression (AR) model, integrated with failure element compensation. The multi-dimension AR model is proposed to build relations between broadband complex signals of array elements in frequency domain and predict the virtual element signals for wideband aperture extension, instead of the traditional time-domain AR model suitable for narrow-band aperture extension. In addition, the broken element compensation algorithm is designed to recover the failure array elements to ensure the feasibility and accuracy of AR modeling and virtual element prediction. Experimental studies verify the effectiveness of the presented approach for broadband array aperture extension.
机译:本文介绍了一种基于多维自动推移(AR)模型的无源声纳阵列的宽带孔径延伸方法,与故障元素补偿集成。提出了多维AR模型来构建频域中阵列元件的宽带复杂信号之间的关系,并预测宽带孔径扩展的虚拟元件信号,而不是适合于窄带孔径延伸的传统时域AR模型。此外,损坏的元件补偿算法旨在恢复故障阵列元件,以确保AR建模和虚拟元件预测的可行性和准确性。实验研究验证了宽带阵列孔径延伸的提出方法的有效性。

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