Comparative Evaluation of Statistical Orbit Determination Algorithms for Short-Term Prediction of Geostationary and Geosynchronous Satellite Orbits in NavIC Constellation

TL;DR

This paper compares various statistical orbit determination algorithms for short-term prediction of NavIC satellites, highlighting the Bootstrap Particle Filter's superior accuracy and ability to handle non-linearity and non-Gaussian noise.

Contribution

It introduces a Bootstrap Particle Filter approach for NavIC satellite orbit prediction and demonstrates its improved accuracy over traditional methods.

Findings

Bootstrap Particle Filter achieves meter-level prediction accuracy.

BPF effectively handles non-linearity and non-Gaussian noise.

Compared methods include LS, EKF, UKF, and EnKF.

Abstract

NavIC is a newly established Indian regional Navigation Constellation with 3 satellites in geostationary Earth orbit (GEO) and 4 satellites in geosynchronous orbit (GSO). Satellite positions are essential in navigation for various positioning applications. In this paper, we propose a Bootstrap Particle Filter (BPF) approach to determine the satellite positions in NavIC constellation for short duration of 1 hr. The Bootstrap Particle filter-based approach was found to be efficient with meter level prediction accuracy as compared to other methods such as Least Squares (LS), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF). The residual analysis revealed that the BPF approach addressed the problem of non-linearity in the dynamics model as well as non-Gaussian nature of the state of the NavIC satellites.