Distributed Sequential Likelihood Ratio Testing for Track Existence Decisions

There are numerous publications on Track-to-Track fusion and Track- to-Track association. 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 is able to reconstruct the global Sequential Likelihood Ratio Test. In this talk, distributed solutions are presented for linear (optimal) and non-linear (sub-optimal) cases.