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一种用于近场源定位的顺序优化校准算法。

A Sequential Optimization Calibration Algorithm for Near-Field Source Localization.

作者信息

Li Jingjing, Yu Xianxiang, Cui Guolong

机构信息

Kexin College Hebei University of Engineering, Handan 056038, China.

School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

出版信息

Sensors (Basel). 2017 Jun 15;17(6):1405. doi: 10.3390/s17061405.

DOI:10.3390/s17061405
PMID:28617324
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5492133/
Abstract

This paper considers the near-field source location problem for a nonuniform linear array (non-ULA) in the presence of sensor gain and phase errors. A sequential optimization calibration method is proposed to simultaneously estimate the gain and phase errors as well as the locations of calibration sources involving the ranges and the azimuths by exploiting some imprecise a-priori knowledge of calibration sources. At each iteration of the proposed method, the source locations, and the gain and phase errors are obtained iteratively. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations. Results show that the proposed algorithm shares the capability to jointly estimate the source locations and the errors.

摘要

本文研究了存在传感器增益和相位误差情况下非均匀线性阵列(non-ULA)的近场源定位问题。提出了一种序贯优化校准方法,通过利用校准源的一些不精确先验知识,同时估计增益和相位误差以及校准源的位置,包括距离和方位角。在所提方法的每次迭代中,迭代获得源位置以及增益和相位误差。最后,在分析阶段,我们通过一些数值模拟评估所提技术的有效性。结果表明,所提算法具有联合估计源位置和误差的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/da8aadd19326/sensors-17-01405-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/55f4efe0ea03/sensors-17-01405-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/91ac328a6f7e/sensors-17-01405-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/c318ee4ea2b9/sensors-17-01405-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/4b58e3b47ab4/sensors-17-01405-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/3c176e02affb/sensors-17-01405-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/ddb620ba9c40/sensors-17-01405-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/44a1c19c8625/sensors-17-01405-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/37c2977f43ee/sensors-17-01405-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/da8aadd19326/sensors-17-01405-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/55f4efe0ea03/sensors-17-01405-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/91ac328a6f7e/sensors-17-01405-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/c318ee4ea2b9/sensors-17-01405-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/4b58e3b47ab4/sensors-17-01405-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/3c176e02affb/sensors-17-01405-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/ddb620ba9c40/sensors-17-01405-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/44a1c19c8625/sensors-17-01405-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/37c2977f43ee/sensors-17-01405-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba0e/5492133/da8aadd19326/sensors-17-01405-g009.jpg

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本文引用的文献

1
Energy-Based Acoustic Source Localization Methods: A Survey.基于能量的声源定位方法:综述
Sensors (Basel). 2017 Feb 15;17(2):376. doi: 10.3390/s17020376.
2
Comparison of Phase-Based 3D Near-Field Source Localization Techniques for UHF RFID.用于超高频射频识别的基于相位的三维近场源定位技术比较
Sensors (Basel). 2016 Jun 25;16(7):978. doi: 10.3390/s16070978.
3
SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization.声罗盘:一种基于分布式 MEMS 麦克风阵列的声源定位传感器。
Sensors (Basel). 2014 Jan 23;14(2):1918-49. doi: 10.3390/s140201918.