Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information

Sensors
Ayham ZaitounyShannon Dee Algar

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.

References

Mar 10, 2001·Physiology & Behavior·I SteinerH P Lipp
Mar 15, 2003·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Kevin Judd
Aug 8, 2009·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Kevin Judd, Thomas Stemler
Jul 10, 2010·Proceedings of the National Academy of Sciences of the United States of America·Ryan LukemanLeah Edelstein-Keshet
Apr 6, 2012·PLoS Computational Biology·Graciano Dieck KattasMichael Small
Jun 27, 2017·Ecology and Evolution·Ayham ZaitounyMichael Small

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LAPACK
MATLAB
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