Classifying several state models using Jeffrey's divergence: Application to target tracking
Clément Magnant, Éric Grivel, Audrey Giremus, Bernard W. Joseph, Laurent Ratton · 2014
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.