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Carnegie Mellon Robotics Institute

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Abstract

Accurate location of people in indoor environments is a key aspect of many applications such as resource management or security. In this paper, we explore the use of short-range radio technologies to track people indoors. The network consists of two kind of radio nodes: static nodes (anchors) and mobile nodes (people). From a set of sparse connectivity matrices (people vs. people and people vs. anchors) at each time instant and people's dynamics, we infer people's trajectories. To combine connectivity and dynamic information, we propose an extension of Multidimensional Scaling (MDS), Dynamic Weighted MDS (DWMDS), that finds an embedding of people's trajectories (x and y coordinates of people through time). DWMDS has proven to be more accurate and efective, especially for low connectivity degree networks (i.e. sparse networks), compared to existing location algorithms. Extensive simulations show the efectiveness and robustness of the proposed algorithm.

Keywords

Multidimensional scaling, visualization, indoor tracking

Notes

Associated Project(s): Component Analysis for Data Analysis

Number of pages: 8

Note: associated to project component analysis for data analysis

Text Reference

Jose Maria Cabero, Fernando De la Torre Frade, I. Arizaga, and A. Sanchez, "Indoor People Tracking based on Dynamic Weighted Multidimensional Scaling," IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, October, 2007.

BibTeX Reference

@inproceedings{Maria_Cabero_2007_5846,
   author = "Jose {Maria Cabero} and Fernando {De la Torre Frade} and I. Arizaga and A. Sanchez",
   title = "Indoor People Tracking based on Dynamic Weighted Multidimensional Scaling",
   booktitle = "IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems",
   month = "October",
   year = "2007",
   Notes = "associated to project component analysis for data analysis"
}

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