Abstract
In the research literature there are numerous publications on Track-to-Track fusion (T2TF) and Track-to-Track association (T2TA). These algorithms assume that the problem of target existence is solved on a per sensor basis. More sophisticated detection methods incorporate the full knowledge of a distributed system by making this decision on a global level. Statistical models that reflect false decisions of individual sensors based on their local data can yield optimal results under linear Gaussian conditions. In particular it has been shown that the Distributed Kalman Filter (DKF) is able to reconstruct the global Sequential Likelihood Ratio Test (SLRT). This, however, is hindered by nonlinear models in many applications. In this paper, this approach is applied to the Federated Kalman filter and to the general setting for T2TA/T2TF such that it can also be applied in non-linear scenarios.
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