Distributed sensor fusion with network constraints

In a practical multi-sensor tracking network, the sensor data processors communicate only a subset of the data available from each sensor, usually in the form of tracks due to constraints in the communications bandwidth. This paper investigates source coding methods by which track information may be communicated with varying levels of available bandwidth with the aim of minimizing the number of bits required by the fusion center for a given estimation error. In particular, we have formulated the distributed sensor fusion problem subject to network constraints as a minimization problem in terms of the Kullback-Leibler distance (KLD). We have shown that for the multi-sensor track fusion problem, the global KLD will be minimized when the individual, local KLDs are minimized. The solutions to some special cases are derived and the losses in the accuracy of the target state estimates that result from the process of source coding and subsequent interpretation is also determined. Simulated results demonstrate the consistency between both theoretical and practical results.