Advanced Bayesian filtering techniques for UWB tracking systems in indoor environments

B. Denis, L. Ouvry, B. Uguen, F. Tchoffo-Talom
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引用次数: 48

Abstract

In this paper, we propose to use Bayesian filtering techniques so as to handle mobility and deal with biased times of arrivals (TOA) in indoor ultra wideband (UWB) positioning systems. More particularly, the modified extended Kalman filter (MEKF) and the modified regularized particle filter (MRPF) enable to track large biases affecting estimated radiolocation metrics due to LOS/NLOS and/or NLOS/NLOS transitions. Simulations were performed with a relevant deterministic UWB propagation model under realistic mobility assumptions. One important corollary for the appealing performances we obtained (e.g. the MEKF provides 90% of time with positioning accuracy better than 0.5 m in a "moderate walk" scenario) is that relaxed synchronization strategies could be adopted in UWB localizers. Indeed, the selection of secondary paths in the TOA estimation procedure, and hence, the introduction of large biases: 1) does not contradict the fine capability of UWB signals to resolve multipath profiles; 2) could enable to extend the range of UWB positioning systems in severe NLOS environments by alleviating the constraints on path detectability; and 3) does not degrade significantly the positioning accuracy if the spatial correlation of TOA biases is taken into account.
室内超宽带跟踪系统的先进贝叶斯滤波技术
在本文中,我们提出使用贝叶斯滤波技术来处理室内超宽带(UWB)定位系统的移动性和到达时间偏差(TOA)。更具体地说,改进的扩展卡尔曼滤波器(MEKF)和改进的正则化粒子滤波器(MRPF)能够跟踪由于LOS/NLOS和/或NLOS/NLOS转换而影响估计无线电定位指标的大偏差。在实际移动假设下,用确定性超宽带传播模型进行了仿真。我们获得的吸引人的性能(例如,在“适度行走”场景中,MEKF提供90%的时间,定位精度优于0.5 m)的一个重要推论是,在UWB定位器中可以采用宽松的同步策略。事实上,在TOA估计过程中,次路径的选择,以及因此引入的大偏差:1)与UWB信号解析多路径曲线的良好能力并不矛盾;2)通过减轻路径可探测性的限制,可以扩展超宽带定位系统在严重NLOS环境下的范围;3)如果考虑TOA偏差的空间相关性,则不会显著降低定位精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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