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

Abstract

This study aims to address the accuracy evaluation problem for rapid transfer alignment with the coexistence of large misalignment angles and uncertain observation noises. For the requirement of accuracy evaluation, complete information in terms of misalignment angles should be estimated during the alignment process. Thus, a fixed-interval smoothing approach is the core of solving this problem. In this paper, a new Divided Difference Filter (DDF)-based an Interacting Multiple Model Two-Filter Smoother (IMM-TFS) is developed to estimate the misalignment angles. The proposed DDF-based IMM-TFS releases the restriction of inverse nonlinearity by using the weighted statistical linearization regression method, and the resulting pseudo-linear model can be used for backward-time IMM filtering. The smoothing step takes into account the merging of estimations and the interaction of multiple models simultaneously. The new smoother is compared with the previous well-known methodologies in simulations. The results show that the DDF-based IMM-TFS can achieve better accuracy for misalignment angles estimation, and has a high efficiency for detecting the changes in a model.