Interacting multiple model methods in target tracking: a survey

1998/01/01 by E. Mazor, A. Averbuch, Amir Averbuch +3 · 1,168 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Bandwidth (computing) #Computational complexity theory #Computer science #Control theory (sociology) #Distributed Sensor Networks and Detection Algorithms #Estimator #Fault Detection and Control Systems #Filter (signal processing) #Mathematical optimization #Mathematics #Quadratic equation #Target Tracking and Data Fusion in Sensor Networks

paper · doi:10.1109/7.640267

published in IEEE Transactions on Aerospace and Electronic Systems 34(1), 103-123 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1998/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• The interacting multiple model algorithm for systems with Markovian switching coefficients

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