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异步到达时间定位系统的定位算法

Localization algorithms for asynchronous time difference of arrival positioning systems.

作者信息

He Shuai, Dong Xiaodai, Lu Wu-Sheng

机构信息

Department of Electrical and Computer Engineering, University of Victoria, 3800 Finnerty Road, Victoria, V8P 5C2 Canada.

出版信息

EURASIP J Wirel Commun Netw. 2017;2017(1):64. doi: 10.1186/s13638-017-0851-1. Epub 2017 Apr 11.

DOI:10.1186/s13638-017-0851-1
PMID:32104170
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7010386/
Abstract

An asynchronous time difference of arrival (ATDOA) positioning system requires no time synchronization among all the anchor and target nodes, which makes it highly practical and can be easily deployed. This paper first presents an ATDOA localization model, and then primarily focuses on two new localization algorithms for the system. The first algorithm is a two-step positioning algorithm that combines semidefinite programming (SDP) with a Taylor series method to achieve global convergence as well as superior estimation accuracy, and the second algorithm is a constrained least-squares method that has the advantage of low complexity and fast convergence while maintaining good performance. In addition, a novel receiver re-selection method is presented to significantly improve estimation accuracy. In this paper, we also derive the Cramer-Rao lower bound (CRLB) of the ATDOA positioning system using a distance-dependent noise variance model, which describes a realistic indoor propagation channel.

摘要

异步到达时间差（ATDOA）定位系统不需要所有锚节点和目标节点之间进行时间同步，这使得它具有很高的实用性且易于部署。本文首先提出了一种ATDOA定位模型，然后主要关注该系统的两种新的定位算法。第一种算法是一种两步定位算法，它将半定规划（SDP）与泰勒级数方法相结合，以实现全局收敛以及卓越的估计精度，第二种算法是一种约束最小二乘法，该方法具有低复杂度和快速收敛的优点，同时保持良好的性能。此外，还提出了一种新颖的接收机重新选择方法，以显著提高估计精度。在本文中，我们还使用距离相关噪声方差模型推导了ATDOA定位系统的克拉美罗下界（CRLB），该模型描述了实际的室内传播信道。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/18b1b130aa05/13638_2017_851_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/b7d353efb858/13638_2017_851_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/9dca24b76af4/13638_2017_851_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/aef5a0de3d57/13638_2017_851_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/16a33905c03a/13638_2017_851_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/49dee46fb44c/13638_2017_851_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/82158dac911b/13638_2017_851_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/ca2861ea552a/13638_2017_851_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/18b1b130aa05/13638_2017_851_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/b7d353efb858/13638_2017_851_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/9dca24b76af4/13638_2017_851_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/aef5a0de3d57/13638_2017_851_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/dbd510acb309/13638_2017_851_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/16a33905c03a/13638_2017_851_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/49dee46fb44c/13638_2017_851_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/82158dac911b/13638_2017_851_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/ca2861ea552a/13638_2017_851_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb11/7010386/18b1b130aa05/13638_2017_851_Fig9_HTML.jpg

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