Reweighting Interacting Multiple-Model Algorithm to Overcome Model Competition for Target Tracking in the Hybrid System

The vicious competition of interacting multiple-model (IMM) algorithm is an inherent problem and would produce irreversible effects on IMM estimation results, especially combining with the radar system. In this article, a novel reweighting IMM (RIMM) is proposed to overcome this issue. First, the theoretical lower bound of model numbers in different situations is respectively provided through the analysis of IMM limitations. Furthermore, certificate the influence of model inaccuracy on the Kalman filter, which illustrates an effective method for reducing errors is increasing model numbers. Third, the definition of model set density and the analysis of the true model space are given, and their connection establishes the standard of how to design the model set or add the model number. Finally, an effective method called RIMM is provided to overcome the competition caused by model increasing. The proposed RIMM holds strong adaptability for different model sets. The simulations of RIMM highlight the correctness and effectiveness of the proposed methods.