Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

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

doi:10.1016/j.procs.2014.05.405

Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

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

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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 language	English
Pages (from-to)	117-124
Number of pages	8
Journal	Procedia Computer Science
Volume	32
DOIs	https://doi.org/10.1016/j.procs.2014.05.405
Publication status	Published - 05 Jun 2014
Externally published	Yes
Event	5th International Conference on Ambient Systems, Networks and Technologies - Hasselt, Belgium Duration: 02 Jun 2014 → 05 Jun 2014

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10.1016/j.procs.2014.05.405Licence: CC BY-NC-ND

Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network
© 2016 The Authors This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited.
Final published version, 402 KBLicence: CC BY-NC-ND

Cite this

@article{4be490c98ad543feae78b38cb500deba,

title = "Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network",

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.",

author = "Rafiullah Khan and Khan, {Sarmad Ullah} and Shahid Khan and Khan, {Mohammad Usman Ali}",

year = "2014",

month = jun,

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doi = "10.1016/j.procs.2014.05.405",

language = "English",

volume = "32",

pages = "117--124",

journal = "Procedia Computer Science ",

issn = "1877-0509",

publisher = "Elsevier B.V.",

note = "5th International Conference on Ambient Systems, Networks and Technologies, ANT 2014 ; Conference date: 02-06-2014 Through 05-06-2014",

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AU - Khan, Rafiullah

AU - Khan, Sarmad Ullah

AU - Khan, Shahid

AU - Khan, Mohammad Usman Ali

PY - 2014/6/5

Y1 - 2014/6/5

N2 - 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.

AB - 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.

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DO - 10.1016/j.procs.2014.05.405

M3 - Article

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JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 5th International Conference on Ambient Systems, Networks and Technologies

Y2 - 2 June 2014 through 5 June 2014

ER -

Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

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