WiFi based indoor localization with multiple kernel learning

Fan, Hui; Chen, Zhongmin

doi:10.1109/iccsn.2016.7587204

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…定位技术有 GPS [1] [2]，红外线 [3]，超声波 [4]，蓝牙 [5]，WiFi [6]等，这些技术运用广泛。测距的方法有 TDOA [7] [8]，TOA [9] [10]，AOA [11]，RSSI [12] 1 2 3 , , …”

Section: 随着物联网相关技术的兴起，基于无线传感网络(Wireless Sensing Network Wsn)和无线局域网 unclassified

Research on Steepest Descent Method Based Indoor Location Estimation Technology

黄¹

2017

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Indoor positioning technology solves the problems that the satellite signal is weak when it reaches the ground and the signal cannot penetrate the structure. Due to the RSSI being limited by multipath effect and the volatility of time, the traditional indoor positioning technology is unstable when positioning. Aiming at the key issues of research in indoor positioning of WSN, a method based on RSSI positioning of the steepest descent algorithm WSN is proposed in the paper. The method uses three access points, namely the reference node, to find the coordinates of the nodes. In addition, the method combines log-normal shadow fading model, trilateral localization algorithm and steepest descent method. Firstly, using the log-normal shadow fading model the value of RSSI has been gotten from the node under test to the reference signal; besides, the trilateral localization algorithm is used to estimate the locating node coordinates; finally, the estimated coordinates are optimized by the optimization method. The simulation results show that, compared with the traditional positioning method based on RSSI, the indoor positioning technology based on the steepest descent method has better positioning accuracy.

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