Multisensor Data Fusion in Adaptive Astro-Satellite-Inertial Navigation System

Abstract

This paper presents the fusion algorithm for navigation information from three distinct sources: strapdown inertial navigation system (SINS), GPS receiver and astronavigation system (ANS) i.e. for SINS/ANS/GPS integrated navigation system in the presence of non- stationary Gaussian noise. Adaptive innovation-based (maximum-likelihood covariance) Kalman filter is used to estimate errors of SINS position, velocity, attitude, accelerometers, gyroscopes and gravitation acceleration to yield improved integrated navigation solution. First order Gauss-Markov processes are used to simulate time-correlation of SINS and GPS errors. Lagrange polynomials are used to interpolate data from each source of navigation information with different output rates.