The Argos-CLS Kalman Filter: Error Structures and State-Space Modelling Relative to Fastloc GPS Data.
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Understanding how an animal utilises its surroundings requires its movements through space to be described accurately. Satellite telemetry is the only means of acquiring movement data for many species however data are prone to varying amounts of spatial error; the recent application of state-space models (SSMs) to the location estimation problem have provided a means to incorporate spatial errors when characterising animal movements. The predominant platform for collecting satellite telemetry data on free-ranging animals, Service Argos, recently provided an alternative Doppler location estimation algorithm that is purported to be more accurate and generate a greater number of locations that its predecessor. We provide a comprehensive assessment of this new estimation process performance on data from free-ranging animals relative to concurrently collected Fastloc GPS data. Additionally, we test the efficacy of three readily-available SSM in predicting the movement of two focal an.....
Page Rangee012475 [16pp.]
Sustainable Development Goals (SDG)SDG14
Maturity LevelTRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space)
Best Practice TypeManual
CitationLowther, A.D.; Lydersen, C.; Fedak, M.A.; Lovell, P. and Kovacs, K.M. (2015) The Argos-CLS Kalman Filter: Error Structures and State-Space Modelling Relative to Fastloc GPS Data. PLoS ONE 10(4): e0124754. DOI:10.1371/journal.pone.0124754
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