Smoothing and predicting celestial pole offsets using a Kalman filter and smoother

Abstract

AbstractIt has been recognized since the early days of interplanetary spaceflight that accurate navigation requires taking into account changes in the Earth’s rotation. In the 1960s, tracking anomalies during the Ranger VII and VIII lunar missions were traced to errors in the Earth orientation parameters. As a result, Earth orientation calibration methods were improved to support the Mariner IV and V planetary missions. Today, accurate Earth orientation parameters are used to track and navigate every interplanetary spaceflight mission. The approach taken at JPL (Jet Propulsion Laboratory) to provide the interplanetary spacecraft tracking and navigation teams with the UT1 and polar motion parameters that they need is based upon the use of a Kalman filter to combine past measurements of these parameters and predict their future evolution. A model was then used to provide the nutation/precession components of the Earth’s orientation. As a result, variations caused by the free core nutation were not taken into account. But for the highest accuracy, these variations must be considered. So JPL recently developed an approach based upon the use of a Kalman filter and smoother to provide smoothed and predicted celestial pole offsets (CPOs) to the interplanetary spacecraft tracking and navigation teams. The approach used at JPL to do this and an evaluation of the accuracy of the predicted CPOs is given here. For assessing the quality of JPL’s nutation predictions, we compare the time series of dX, dY provided by JPL with the predictions obtained from the IERS Rapid Service/Prediction Centre. Our results confirmed that the approach recently developed by JPL can be used for the successful nutation prediction. In particular, we show that after 90 days of prediction, the estimated errors are 43% lower for dX and 33% lower for dY than in the case of the official IERS products, and an average improvement is 19% and 22% for dX and dY, respectively.