Multiple model tracking using fuzzy clustering

Tracking maneuvering targets has always been a significant challenge to the tracking community, so new approaches to this problem are always being pursued. One approach is to use multiple model filters as an attractive design logic for both maneuver detection and filter re-initialization. Common current practice in multiple model tracking uses a switching Markov model. A well known multiple model tracker that uses Markov switching model is the Interactive Multiple Model (IMM). This approach requires the a priori knowledge of the transition probability matrix (TPM) of the target state. Such knowledge may not be available unless one has combat identification, so one is usually dealing with target of unknown maneuver strategies. The objective of this paper is to introduce concepts from fuzzy sets to design a multiple model filter which is applicable to an arbitrary number of target models while at the same time not requiring the usage of the Maxkov switching to transition between the threat models. The essential concept is to treat each possible target dynamics as a fuzzy cluster, then to use the measurement information about the target to compute the degree of membership the target has relative to a particular fuzzy cluster. Such membership value would then be the equivalent of the switching gain when using IMM terminology. The target state is the weighted sum of the states provided by each individual filter.