[MARMAM] New publication on performance of Bayesian State-Space Models fit to Argos data processed with Kalman Filtering

Mónica Cordeiro de Almeida e Silva monica at uac.pt
Tue Mar 25 13:55:20 PDT 2014


Dear Colleagues,

 

The following paper entitled "Assessing Performance of Bayesian State-Space Models Fit to Argos Satellite Telemetry Locations Processed with Kalman Filtering" was recently published in PLOS ONE and is available online at http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0092277.

 

Silva MA, Jonsen I, Russell DJF, Prieto R, Thompson D, et al. (2014) Assessing Performance of Bayesian State-Space Models Fit to Argos Satellite Telemetry Locations Processed with Kalman Filtering. PLoS ONE 9(3): e92277. doi:10.1371/journal.pone.0092277

 

 

Abstract

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.

 

Kind regards,

 

Mónica Almeida e Silva

(Marine Biologist, PhD)

-----------------------------------------------------

IMAR - Institute of Marine Research

University of the Azores

9901-862 Horta Portugal

Phone: (+351) 292200400

http://www.whales.uac.pt/

-----------------------------------------------------

Guest Investigator 

WHOI - Woods Hole Oceanographic Institution

Woods Hole, MA 02543, USA

 

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