AMoveE 2014: David Douglas
Channel: Movebank
Date: 2014-05-20

This talk was presented by David Douglas on 6 May 2014 as part of the Symposium on Animal Movement and the Environment, held at the North Carolina Museum of Natural Sciences in Raleigh.

Douglas D, 2014, "Accuracy assessment of Argos Kalman filter location processing"

Abstract: In 2011, CLS (Service Argos) introduced a new method for estimating locations of Doppler-type satellite transmitters. Compared to the legacy least-squares (LS) processing method, the new method applies a Kalman filter (KF) and a user-defined maximum transmitter speed to constrain location errors and to provide location estimates from single-message satellite overpasses. Unless users specifically instruct CLS otherwise, KF processing is applied to new tracking applications with default transmitter speeds of 10 m/s, 16 m/s, and 16 m/s for marine animal, terrestrial animal, and bird tracking, respectively. We used tracking data (Doppler and GPS) obtained from 12 polar bears and 9 bald eagles to compare the number and accuracy of locations derived with LS and KF processing. Accuracy of the LS and KF locations were judged using coincident (+/- 30 min) GPS locations. Compared to LS, KF processing produced 59% and 11% more bear and eagle locations respectively. While KF processing generated modest increases in the number of standard quality (LC 3, 2, or 1) locations, the majority (90% and 70%, respectively) of additional bear and eagle locations produced by KF were those derived from 1-message overpasses. The polar bear transmitters were predisposed to disproportionately benefit from KF processing owing to atypically large transmission pulse rates (130-200 seconds). Among all animals, accuracy (defined as the 67th percentile of the pooled error distribution) of standard quality locations in the raw KF data was somewhat poorer (10% larger error) compared to raw LS data, but accuracy among auxiliary classes (LC 0, A, B) in the KF data was substantially improved (40% smaller error). Although raw KF auxiliary location classes have markedly improved accuracy, applying a filtering strategy to constrain the upper error distribution remains advisable and effective. We present comparative results of filtering LS and KF location data using 3 approaches: the Douglas Argos-filter algorithm, a Bayesian state-space model, and a continuous-time correlated random walk model.