Efficient algorithm for fusion of hierarchically structured data

Suppose that several sensors simultaneously (or a single sensor sequentially) observe(s) the same n objects and produce(s) m reports of estimates of their types. Here, the only allowed types are associated with a given hierarchical structure that is represented by a rooted tree. We present an efficient algorithm for fusing the received m observation reports and producing estimates for the true underlying n object types. We first present an optimal algorithm for solving the above problem that generates an hypothesis set of all possible solutions. However, the cardinality of this hypothesis set becomes prohibitively large as the problem size grows. Nevertheless, we could use this approach to solve rather small problems that we used for comparison purposes. Then, based on the optimal algorithm we propose a suboptimal algorithm that judiciously generates a substantially reduced hypotheses set. Simulation results reveal that by using the latter algorithm, we can quite accurately estimate the true n object types for relatively large problems. Finally, we present an example that demonstrates the execution of the proposed algorithm.