Distributed multiple model extended information filter with unbiased mixing for satellite launch vehicle tracking

Abstract

A distributed extended information filter-based interacting multiple model estimator with unbiased mixing is proposed for satellite launch vehicle tracking. In this problem, multiple heterogeneous sensors such as radars, telemetry systems receiving onboard Global Positioning System—inertial navigation system data, and electro-optical targeting systems are used. The extended information filter is used for nonlinear estimation dealing with ballistic model and spherical coordinate observation. The multiple Markov switching models comprise thrusting and coasting modes having different state vector dimensions for the launch vehicle. To effectively combine both state vectors, an unbiased mixing technique is applied and then the distributed extended information filter integrates local states and information matrix contributions. Hence, the proposed algorithm takes into account both heterogeneity of tracking sensors and multiplicity of vehicle’s dynamic model. We prove the superiority of the proposed algorithm by conducting Monte Carlo simulation with nominal trajectory data of Korea Space Launch Vehicle-1. Comparative simulation results demonstrate that the performance of the proposed method has been improved in vehicle’s position root mean square error.