Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

Rafiullah Khan, Sarmad Ullah Khan, Shahid Khan, Mohammad Usman Ali Khan

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)
166 Downloads (Pure)

Abstract

This paper evaluates the positioning and tracking p8erformance of Extended Kalman Filter (EKF) in wireless sensors network. The EKF is a linear approximation of statistical Kalman Filter (KF) and has the capability to work efficiently in non-linear systems. The EKF is based on an iterative process of estimating current state information from the previously estimated state. Its working is based on the linearization of observation model around the mean of current state information. The EKF has small computation complexity and requires low memory compared to other Bayesian algorithms which makes it very suitable for low powered mobile devices. This paper evaluates the localization and tracking performance of EKF for (i) Position (P) model, (ii) Position-Velocity (PV) model and (iii) Position-Velocity-Acceleration (PVA) model. The EKF processes distance measurements from cricket sensors that are acquired through time difference of arrival between ultrasound and Radio Frequency (RF) signals. Further, localization performance under varying number of beacons/sensors is also evaluated in this paper.
Original languageEnglish
Pages (from-to)117-124
Number of pages8
JournalProcedia Computer Science
Volume32
DOIs
Publication statusPublished - 05 Jun 2014
Externally publishedYes
Event5th International Conference on Ambient Systems, Networks and Technologies - Hasselt, Belgium
Duration: 02 Jun 201405 Jun 2014

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