DATA FUSION AND STABILITY ANALYSIS WITH ALPHA FAMILY METHOD APPROXIMATION ESTIMATORS FOR SATELLITE ORBIT DETERMINATION

Abstract

Accurate and robust satellite orbit determination is essential for space missions, navigation, and remote sensing. This paper presents a comparative analysis of three nonlinear state estimation techniques like Extended Kalman Filter (EKF), Particle Filter (PF), and the Alpha Filter (AF) focusing on their performance in estimating satellite position and velocity under noisy conditions. A simulated orbit scenario for Low earth orbit (LEO) is used, with Gaussian noise added to sensor measurements to introduce real-world uncertainties. Here, the Satellite orbit-determination (OD) process is described and presents the results of numerical simulation of Satellite orbit estimation using Alpha Family Method Approximation estimators (AFMAE’s). Additionally, a proposed observer for a nonlinear continuous time dynamic system makes use of the corresponding matrix Riccati type differential equation and gain from the theories of the AFMAE. Then, the condition for the local asymptotic stability for the error dynamics of the observer is derived using the Lyapunov energy (LE) functional. AFMAE-based sensor data fusion methodology is introduced. A data fusion scheme that utilizes the AFMAE’s for unified state model (U7) and inertial coordinate set (IC6) orbital trajectories is presented in state vector level fusion (SVF). State vector level Data fusion is illustrated by implementing two AFME’s. All the filtering schemes and data fusion algorithms have been validated using simulated data generated in MATLAB.