Position Quantization Approach with Multi-class Classification for Wi-Fi Indoor Positioning System

Abstract

Indoor positioning system is a challenging problem due to the variety of environment and unreliable of data that are used for a prediction of the position. For Wi-Fi based indoor positioning system, signal intensity used to predict the co-ordinate of the device are known to fluctuate greatly despite being measured at the same position. Therefore, significant errors are often found when solving this problem with regression algorithms. A quantization of co-ordinate data into position IDs can mitigate the fluctuated noises in the data and is able to reformulate the problem into a multi-class classification problem. The error in positioning can then be computed from the distance between the true co-ordinate and the predicted co-ordinate. The experiment shows that Random forest classification can predict the position with the error in positing at 5.65 meters on average when the quantization is applied with threshold setting to 1 meter