A ZigBee indoor positioning scheme using signal-index-pair data preprocess method to enhance precision

Abstract

This paper develops a ZigBee indoor positioning scheme based on the location fingerprinting approach. The proposed scheme includes four workflows: (1) creating the location fingerprint table, (2) training the locating model using neural network (NN), (3) preprocessing data through the Signal-Index-Pair method, and (4) estimating the coordinate of the mobile target instantly. Testing results show that within the error distance of 5 meters, the NN locating model with the Signal-Index-Pair data preprocess method can increase the positioning precision by 17% compared with the original NN, in terms of the cumulative error probability (CEP). It also achieves 5% CEP higher than the k (k=5) nearest neighbor method and the weighted k (k=5) nearest neighbor method. Potential applications include patient tracking in hospitals, object tracking for factory monitoring, self-navigation of autonomous robots, and visitors monitoring in military buildings, and so on.