Title:Indoor Positioning Using WSN and INS Sensor Fusion
VOLUME: 4 ISSUE: 1
Author(s):Ehad Akeila, Zoran Salcic and Akshya Swain
Affiliation:Department of Electrical and Computer Engineering. University of Auckland New Zealand.
Keywords:Bluetooth, indoor tracking, inertial navigation system, Kalman Filter, particle filter, sensor fusion, wireless sensor
network.
Abstract:This paper proposes a new method for enhancing the accuracy and availability of positioning
in indoor environment based on fusion of the positioning results obtained from two sensing technologies,
Inertial Navigation System (INS) and Wireless Sensor Network (WSN) (in this case Bluetooth-
based positioning system). The performance of each individual sensing technology has been optimised
first using new positioning methods before performing the fusion of results. The fusion is
achieved by the utilisation of two fusion filters, particle filter and Extended Kalman filter, and is thoroughly
tested using a portable data acquisition unit which is specially designed for that purpose. Detailed
analysis and comparison of the performance between particle filter and Extended Kalman filter
has been performed. Results show that the proposed fusion method reduces the errors compared to single sensing technology
and the mean distance error of the final fused system is maintained below 1 metre using either of the two filters.
