.png){kind=icon}
返回
Motion Parameter Estimation for Mobile Sources Using Semidefinite Programming
.png){kind=icon}
PRE
AI
DOI：10.1109/TMC.2021.3084595.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we address the motion parameter estimation problem using time difference of arrival (TDOA) measurements, where a mobile source starts from an initial position with constant velocity. The problem is formulated as three non-convex constrained weighted least squares (CWLS) problems. Owing to their non-convex nature, local convergence may occur when solving them by an iterative algorithm, implying good estimates are not guaranteed. We propose to solve these CWLS problems by applying semidefinite relaxation (SDR) to generate three different semidefinite programming (SDP) problems, namely, the motion unconstrained semidefinite programming (MU-SDP), motion constrained semidefinite programming (MC-SDP), and motion parameter direct semidefinite programming (MPD-SDP). The MU-SDP, MC-SDP, and MPD-SDP methods are then extended to the scenario of unknown propagation speed (PS), which is common in underwater and underground localizations. Specifically, for this scenario, the two-step MU-SDP, two-step MC-SDP, and two-step MPD-SDP methods are designed to keep the relaxed SDP problems tight by introducing the penalty function. Simulation results show that MU-SDP performs worse than MC-SDP and MPD-SDP owing to the ignorance of the motion equation, and MC-SDP and MPD-SDP are able to reach the Cramer-Rao lower bound (CRLB) accuracy.
Keyword:
Sensors
Location awareness
Programming
Velocity measurement
Mobile computing
Mathematical model
Time difference of arrival
Motion parameter estimation
constrained weighted least squares
semidefinite programming
time difference of arrival
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.2
论文数:
5.9K
被引数:
1.8W
机构
引用论文
.png){kind=icon}
分享收藏
A Survey on Mobile Anchor Node Assisted Localization in Wireless Sensor Networks无线传感器网络中移动锚节点辅助定位研究综述
PREAI
分享收藏
查看更多内容