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A Method for Investigating Change Blindness in Pigeons Columba Livia

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Tracking a single pigeon using a shadowing filter algorithm.

Ayham Zaitouny^1,2, Thomas Stemler^1,3, Michael Small^1,2

¹School of Mathematics and Statistics University of Western Australia Crawley WA Australia.

Ecology and Evolution

|June 27, 2017

Summary

This summary is machine-generated.

This study introduces a shadowing filter algorithm to accurately reconstruct animal movement dynamics from GPS data. The new method minimizes errors and extracts full movement states like velocity and acceleration, improving upon existing techniques.

Keywords:

GPS animal behavior animal movement filtering shadowing filter tracking

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Area of Science:

Animal movement ecology
Biophysics
Data science

Background:

Miniature GPS devices provide high-resolution animal tracking data.
Existing methods struggle with measurement errors and extracting full dynamical states (velocity, acceleration) from position data alone.
Direct differentiation of position data is often inadequate for accurate state estimation.

Purpose of the Study:

To implement and validate a shadowing filter algorithm for reconstructing the full dynamical state space of animal movement.
To minimize measurement errors inherent in GPS tracking.
To provide a practical prototype for applying Newtonian dynamics-based filtering to real-world animal tracking data.

Main Methods:

Development and application of a shadowing filter algorithm based on Newtonian dynamics.
Minimization of measurement errors from GPS position recordings.
Reconstruction of velocity and acceleration from position data for a flying pigeon.

Main Results:

Successful implementation of the shadowing filter algorithm.
Demonstrated minimization of measurement errors.
Accurate reconstruction of the full phase-space (position, velocity, acceleration) from GPS data.
Exploration of filter parameter selection and minimum data requirements.

Conclusions:

The shadowing filter offers a robust method for enhancing animal tracking data quality and extracting detailed movement dynamics.
This approach provides a practical solution for overcoming limitations of standard GPS data analysis.
The study presents a prototype for wider application of shadowing filters in animal movement studies.