Improving Argos Doppler Location Using Multiple-Model Kalman Filtering

Abstract

The Argos service was launched in 1978 to serve environmental applications, including oceanography, wildlife tracking, fishing vessel monitoring, and maritime safety. The system allows for worldwide near-real-time positioning and data collection of platform terminal transmitters (PTTs). The positioning of the PTTs is achieved by exploiting the Doppler shift in the carrier frequency of the transmitter as recorded by satelliteborne Argos receivers. Until March 15, 2011, a classical nonlinear least squares estimation technique was systematically used to estimate Argos positions. Since then, a second positioning algorithm using a multiple-model Kalman filter was implemented in the operational Argos positioning software. This paper presents this new algorithm and analyzes its performance using a large data set obtained from over 200 mobiles carrying both an Argos transmitter and a GPS receiver used as ground truth. The results show that the new algorithm significantly improves the positioning accuracy, particularly in difficult conditions (for class-A and class-B locations, in the Argos terminology). Moreover, the new algorithm enables the retrieval of a larger number of estimated positions and the systematic estimation of the location error.