Indoor positioning using particle filters with optimal importance function

Leila Pishdad, F. Labeau
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引用次数: 3

Abstract

Particle filters have been widely used in positioning problems, to post-process the noisy location sensor measurements. In this paper, instead of the commonly used Prior Importance Function for particle filtering, we have formulated and applied the Optimal Importance Function. Unlike other importance functions, the Optimal Importance Function minimizes the variance of particle weights and thus resolves the degeneracy problem of particle filters. In this work, we have derived a closed form formula for the Optimal Importance Function using map-independent random walk velocity motion model and a GMM sensor error. Due to the generality of the proposed method, it can be used for a wide range of moving objects in different environments. Simulation results support the validity of modeling assumptions and the advantage of applying an Optimal Importance Function in indoor localization and positioning.
采用具有最优重要函数的粒子过滤器进行室内定位
粒子滤波被广泛应用于定位问题中,用于对有噪声的定位传感器测量结果进行后处理。本文用最优重要函数来代替常用的先验重要函数来进行粒子滤波。与其他重要函数不同的是,最优重要函数最小化了粒子权重的方差,从而解决了粒子滤波器的退化问题。在这项工作中,我们使用与地图无关的随机行走速度运动模型和GMM传感器误差推导了最优重要性函数的封闭形式公式。由于所提出的方法的通用性,它可以用于不同环境中的各种运动物体。仿真结果验证了模型假设的有效性和最优重要函数在室内定位中的优越性。
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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