Interacting multiple model methods in target tracking: a survey

TLDR

The Interacting Multiple Model (IMM) estimator is a cost‑effective hybrid filter that can estimate the state of dynamic systems with multiple switching behavior modes, offering near‑linear computational complexity while achieving performance close to quadratic‑complexity algorithms, making it well suited for tracking maneuvering targets. This work surveys and contextualizes existing IMM methods for target tracking problems. The survey emphasizes the assumptions underlying each algorithm and their applicability to various tracking scenarios.

Abstract

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations.