Classifying several state models using Jeffrey´s divergence: Application to target tracking

One of the most important challenges when applying multiple-model approaches is model-set design. However, to our knowledge, there are not general rules to choose models. For this purpose, we recently proposed an approach based on the Jeffrey´s divergence to characterize dissimilarities between two state models. In this paper, our contribution is to use the Jeffrey´s divergence to classify at least two models into subsets. Our approach consists in creating a dissimilarity matrix composed of Jeffrey´s divergences between model pairs. Then, we transform this matrix to get a correlation-like matrix and an eigenvalue decomposition is computed. We propose an interpretation of the predominant eigenvalues and use it to deduce the number of model subsets and their cardinals. Finally, a classification algorithm can be considered to determine which models belong to which subsets. Among the applications, we focus on target tracking.