Optimal Shadowing Filter for Positioning and Tracking Methodology with Limited Information

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target's position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter's performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.
Original languageEnglish
Article number931
JournalSensors
Volume19
Issue number4
DOIs
Publication statusPublished - 22 Feb 2019

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positioning
Noise
Trajectories
methodology
Sampling
filters
sampling
trajectories
estimates

Cite this

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title = "Optimal Shadowing Filter for Positioning and Tracking Methodology with Limited Information",
abstract = "Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target's position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter's performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.",
author = "Ayham Zaitouny and Thomas Stemler and Shannon Algar",
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Optimal Shadowing Filter for Positioning and Tracking Methodology with Limited Information. / Zaitouny, Ayham; Stemler, Thomas; Algar, Shannon.

In: Sensors, Vol. 19, No. 4, 931, 22.02.2019.

Research output: Contribution to journalArticle

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AB - Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target's position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter's performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.

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