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Weighted K-nearest Neighbor Fast Localization Algorithm Based on RSSI for Wireless Sensor Systems

[ Vol. 13 , Issue. 2 ]

Author(s):

Lu Bai*, Chenglie Du and Jinchao Chen   Pages 295 - 301 ( 7 )

Abstract:


Background: Wireless positioning is one of the most important technologies for realtime applications in wireless sensor systems. This paper mainly studies the indoor wireless positioning algorithm of robots.

Methods: The application of the K-nearest neighbor algorithm in Wi-Fi positioning is studied by analyzing the Wi-Fi fingerprint location algorithm based on Received Signal Strength Indication (RSSI) and K-Nearest Neighbor (KNN) algorithm in Wi-Fi positioning. The KNN algorithm is computationally intensive and time-consuming.

Results: In order to improve the positioning efficiency, improve the positioning accuracy and reduce the computation time, a fast weighted K-neighbor correlation algorithm based on RSSI is proposed based on the K-Means algorithm. Thereby achieving the purpose of reducing the calculation time, quickly estimating the position distance, and improving the positioning accuracy.

Conclusion: Simulation analysis shows that the algorithm can effectively shorten the positioning time and improve the positioning efficiency in robot Wi-Fi positioning.

Keywords:

K-nearest neighbor, Wi-Fi positioning, RSSI, wireless sensor system, location fingerprint positioning, K-Means.

Affiliation:

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, School of Computer Science, Northwestern Polytechnical University, Xi’an 710072

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