Data association based on optimization in graphical models with application to sensor networks

We propose techniques based on graphical models for efficiently solving data association problems arising in multiple target tracking with distributed sensor networks. Graphical models provide a powerful framework for representing the statistical dependencies among a collection of random variables, and are widely used in many applications (e.g., computer vision, error-correcting codes). We consider two different types of data association problems, corresponding to whether or not it is known a priori which targets are within the surveillance range of each sensor. We first demonstrate how to transform these two problems to inference problems on graphical models. With this transformation, both problems can be solved efficiently by local message-passing algorithms for graphical models, which solve optimization problems in a distributed manner by exchange of information among neighboring nodes on the graph. Moreover, a suitably reweighted version of the max-product algorithm yields provably optimal data associations. These approaches scale well with the number of sensors in the network, and moreover are well suited to being realized in a distributed fashion. So as to address trade-offs between performance and communication costs, we propose a communication-sensitive form of message-passing that is capable of achieving near-optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data.