Distributed Data Fusion Using Support Vector Machines

The basic quantity to be estimated in the Bayesian approach to data fusion is the conditional probability density function (CPDF). In recent times, computationally efficient particle filtering approaches are gaining growing importance in estimating these CPDF. In this approach, i.i.d samples are used to represent the conditional probability densities. However, their application in data fusion is severely limited due to the fact that the information is stored in the form of a large set of samples. In all practical data fusion systems that have limited communication bandwidth, broadcasting this probabilistic information, available as a set of samples, to the fusion center is impractical. Support vector machines, through statistical learning theory, provide a way of compressing information by generating optimal kernal based representations. In this paper we use SVM to compress the probabilistic information available in the form of i.i.d samples and apply it to solve the Bayesian data fusion problem. We demonstrate this technique on a multi-sensor tracking example.