Lu Bai*, Chenglie Du and Jinchao Chen Pages 295 - 301 ( 7 )
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.
K-nearest neighbor, Wi-Fi positioning, RSSI, wireless sensor system, location fingerprint positioning, K-Means.
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