IMM-UKF-TFS Model-based Approach for Intelligent Navigation

Abstract

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

Radar and Automatic Identification System Track Fusion in an Electronic Chart Display and Information System

“…The main idea of these is to choose the best for the present situation of elementary Kalman filters. This can be done via adaptive estimation, decision-based methods or other multiple model approaches such as the Interacting Multiple Model (IMM) filter (Tuzlukov, 2013), by using interval Kalman filtering (Motwani et al, 2013) or another solution (Malleswaran et al, 2013; Wang et al, 2013a).…”

A DDF-based IMM-TFS Approach for the Accuracy Evaluation Problem of Rapid Transfer Alignment

“…The structure of the IMM-RTS Smoother (IMM-RTSS) is more like the standard IMM filter, which means that it is easily realised. In contrast, the IMM Two-Filter-Smoother (IMM-TFS) utilises the observations more effectively, and has better performance for solving nonlinear smoothing problems (Malleswaran et al, 2013). Hence, IMM-TFS is used in this paper due to its advantages.…”

“…An inherent drawback of IMM-TFS is that it needs the existence of an inverse model, which is either predefined or obtained from trivial derivations in previously published literatures (Helmick et al, 1995; Malleswaran et al, 2013). This restriction limits the application of IMM-TFS in practice.…”