Modeling and Estimation for Maneuvering Target Tracking with Inertial Systems using Interacting Multiple Models

Abstract

The aim of this Thesis is to study and develop Estimation Technique that enhances the Dynamic Tracking capability of Maneuvering Targets based using Inertial Systems. Inertial Measurement Systems have measurement biases and drifts and properly estimating their errors is a real time problem. Moreover, different targets perform different types of maneuvers during different stages of their trajectory and as such it is not possible to obtain accurate tracking of target maneuvers using a filters based on conventional single model approach. As such, a technique is required which is dynamic in both estimating and filtering the errors in inertial measurements and in switching to appropriate motion models according to the current maneuver of the vehicle. This thesis suggests and evaluates ‘Interacting Multiple Models (IMM)’ scheme for the solution to the above problem. Performance of the IMM scheme is proven over conventional single model based filters like Kalman Filter through both simulations and real target tracking.