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An introduction to support Vector Machines: and other kernel-based learning methods
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IEEE Communications Magazine
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IEEE Communications Magazine
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Accurate and correct indoor positioning in wireless networks could provide interesting services and applications in many fields. There are Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), and location fingerprinting schemes that can be used for positioning. We choose location fingerprinting in this paper because it is more applicable to complex indoor environments than other schemes. Location fingerprinting uses received signal strength (RSS) to estimate locations of mobile nodes or users. Probabilistic method and k-Nearest-Neighbor (kNN) are previously proposed positioning techniques based on location fingerprinting. However, these two techniques only use the mean of received signal strength at positioning stage, and the standard deviation of received signal strength is ignored and not considered when positioning. Therefore, the positioning accuracy can hardly obtain further improvement. In this paper, we firstly proposed a novel positioning technique which is based on Overlap Area Matching Algorithm in which the mean and standard deviation of received signal strength are considered both at sampling stage and at positioning stage. Then we presented Pascal source code of Overlap Area Matching Algorithm in order to understand the proposed positioning technique more clearly. Furthermore, the experimental testbed, experimental results and comparisons among the kNN, Probabilistic and Overlap Area Matching were discussed. Experimental results show that the proposed positioning algorithm in this paper can improve the positioning accuracy remarkably. Finally, we summarized the paper and gave possible future directions for research on wireless positioning techniques for indoor environments.