文章基本信息

标题：Sequential Multidimensional Scaling with Kalman Filtering for Location Tracking
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作者：Lan Anh Trinh ; Nguyen Duc Thang ; Dang Viet Hung 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/584912
出版社：Hindawi Publishing Corporation
摘要：Localization always plays a critical role in wireless sensor networks for a wide range of applications including military, healthcare, and robotics. Although the classical multidimensional scaling (MDS) is a conventionally effective model for positioning, the accuracy of this method is affected by noises from the environment. In this paper, we propose a solution to attenuate noise effects to MDS by combining MDS with a Kalman filter. A model is built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution. From that, a linear tracking system is developed. The characteristics of the algorithm are examined through simulated experiments and the results reveal the advantages of our method over conventional works in dealing with the above challenges. Besides, the method is simplified with a linear filter; therefore it suits small and embedded sensors equipped with limited power, memory, and computational capacities well.