波束赋形
反褶积
稳健性(进化)
最小方差无偏估计量
声学
噪音(视频)
协方差矩阵
到达方向
计算机科学
梁(结构)
信号(编程语言)
物理
光学
算法
数学
电信
统计
均方误差
人工智能
化学
程序设计语言
图像(数学)
基因
生物化学
天线(收音机)
摘要
Horizontal arrays are often used to detect/separate a weak signal and estimate its direction of arrival among many loud interfering sources and ambient noise. Conventional beamforming (CBF) is robust but suffers from fat beams and high level sidelobes. High resolution beamforming such as minimum-variance distortionless-response (MVDR) yields narrow beam widths and low sidelobe levels but is sensitive to signal mismatch and requires many snapshots of data to estimate the signal covariance matrix, which can be a problem for a moving source. Deconvolution algorithm used in image de-blurring was applied to the conventional beam power of a uniform line array (spaced at half-wavelength) to avoid the instability problems of common deconvolution methods and demonstrated with real data (T. C. Yang, IEEE J. Oceanic Eng., 43, 160–172, 2018). The deconvolved beam output yields narrow beams, and low sidelobe levels similar to MVDR and at the same time retains the robustness of CBF. It yields a higher output signal-to-noise ratio than MVDR for isotropic noise. The method is applied here to the horizontal array data collected during the SWellEx96 experiment. Bearing time record are created to compare the performance of various beamforming methods and used to track the source position.
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