Sequential likelihood ratio test based on the Distributed Kalman Filter for multi sensor track extraction

In the research literature there are numerous publications on Track-to-Track fusion and Track-to-Track association. All these algorithms assume that the problem of target existence is solved on a per sensor basis. In this paper the distributed computation of the track existence by means of the likelihood ratio score is presented. The approach is based on the Distributed Kalman Filter where a single target likelihood function is used which allows to model uniformly distributed clutter with a fixed clutter rate. Numerical examples show that the fused track existence score is close to equivalent to a centralized approach.