Radar target tracking-Viterbi versus IMM

The performance of the Viterbi and the interacting multiple model (IMM) algorithms, applied to radar tracking and detection, are investigated and compared. Two different cases are considered. In the first case, target acceleration is identical to one of the system models while in the second case it is not. The performance of the algorithms depends monotonically on the maximal magnitude of the difference between models acceleration, on the time interval between measurements, and on the reciprocal of the measurements error standard deviation. In general, when these parameters are relatively large, both algorithms perform well. When they are relatively small, the Viterbi algorithm is better. However, during delay periods, namely right after the start of a maneuver, the IMM algorithm provides better estimations     

Interacting multiple model methods in target tracking: a survey

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