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首页> 外文期刊>Journal of network and computer applications >Kernel-based particle filtering for indoor tracking in WLANs
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Kernel-based particle filtering for indoor tracking in WLANs

机译：基于内核的粒子过滤，用于WLAN中的室内跟踪

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摘要

Indoor localization using signal strength in wireless local area networks (WLANs) is becoming increasingly prevalent in today's pervasive computing applications. In this paper, we propose an indoor tracking algorithm under the Bayesian filtering and machine learning framework. The main idea is to apply a graph-based particle filter to track a person's location on an indoor floor map, and to utilize the machine learning method to approximate the likelihood of an observation at various locations based on the calibration data. Nadaraya-Watson kernel regression is adopted to interpolate the Received Signal Strength (RSS) distribution for nonsurvey points. The success of the proposed kernel-based particle filter (KBPF) lies in the fact that KBPF incorporates the environmental and motion constraints into the model and restricts particles to propagate on the graph which precludes the locations that the person is unlikely to be at, and that the developed nonlinear interpolation method is effective in inferring the RSS distribution for the non-survey location points which makes it possible to reduce the total number of survey locations. In addition, missing value problem is addressed in this paper, and different methods are compared through experiments. We conducted a series of experiments in a typical office environment. Results show that KBPF achieves superior performance than other existing algorithms. It even yields higher accuracy with only a small fraction of training data than others with a full training data set. As a consequence, by applying KBPF, sub-meter accuracy can be obtained while extensive calibration effort can be greatly reduced. Although KBPF is more computationally complex, it can still provide real time estimates.
机译：在当今的普及计算应用中，使用信号强度在无线局域网（WLAN）中进行室内定位正变得越来越普遍。在本文中，我们提出了一种基于贝叶斯过滤和机器学习框架的室内跟踪算法。主要思想是应用基于图形的粒子过滤器来跟踪人在室内楼层地图上的位置，并利用机器学习方法根据校准数据来估计在各个位置观察的可能性。采用Nadaraya-Watson核回归来内插非测量点的接收信号强度（RSS）分布。所提出的基于核的粒子过滤器（KBPF）的成功在于，KBPF将环境和运动约束纳入模型中，并限制了粒子在图形上传播，从而排除了人不太可能位于的位置，并且所开发的非线性插值方法可以有效地推断非调查位置点的RSS分布，从而可以减少调查地点的总数。此外，本文还解决了价值缺失问题，并通过实验比较了不同的方法。我们在典型的办公环境中进行了一系列实验。结果表明，KBPF比其他现有算法具有更高的性能。与只有完整训练数据集的数据相比，仅使用一小部分训练数据就可以产生更高的准确性。结果，通过应用KBPF，可以获得亚表精度，同时可以大大减少大量的校准工作。尽管KBPF在计算上更加复杂，但它仍可以提供实时估计。

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