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State-space models are important tools for quality control of error-proneanimal movement data. The near real-time (within 24 h) capability of the Argossatellite system aids dynamic ocean management of human activities by informingwhen animals enter intensive use zones. This capability also facilitates use ofocean observations from animal-borne sensors in operational ocean forecastingmodels. Such near real-time data provision requires rapid, reliable qualitycontrol to deal with error-prone Argos locations. We formulate acontinuous-time state-space model for the three types of Argos location data(Least-Squares, Kalman filter, and Kalman smoother), accounting for irregulartiming of observations. Our model is deliberately simple to ensure speed andreliability for automated, near real-time quality control of Argos data. Wevalidate the model by fitting to Argos data collected from 61 individualsacross 7 marine vertebrates and compare model-estimated locations to GPSlocations. Estimation accuracy varied among species with median Root MeanSquared Errors usually < 5 km and decreased with increasing data sampling rateand precision of Argos locations. Including a model parameter to inflate Argoserror ellipse sizes resulted in more accurate location estimates. In somecases, the model appreciably improved the accuracy of the Argos Kalman smootherlocations, which should not be possible if the smoother uses all availableinformation. Our model provides quality-controlled locations from ArgosLeast-Squares or Kalman filter data with slightly better accuracy than ArgosKalman smoother data that are only available via reprocessing. Simplicity andease of use make the model suitable both for automated quality control of nearreal-time Argos data and for manual use by researchers working with historicalArgos data.

A continuous-time state-space model for rapid quality-control of Argos locations from animal-borne tags