LAUSR

Distributed scenarios pose a big challenge to tracking and fusion systems. They require the prevention of repeatedly incorporating the same information, which originates from ring closures in the communication path and would affect optimality. Additionally, the multi-sensor multi-object Generalized Labeled Multi-Bernoulli filter update is NP-hard in principle. The method proposed in this paper tackles these problems, as it constitutes a divide and conquer strategy for distributed, synchronized multi-sensor systems with central fusion. Based on a common prediction, local sensor updates are calculated separately, sent back and fused centrally in order to start a new cycle. Thus, the intractable multi-sensor update is split into less complex local single-sensor updates and a novel, low-complexity fusion strategy. The proposed method enables a full parallelization of the optimal multi-sensor Generalized Labeled Multi-Bernoulli and $\delta$-Generalized Labeled Multi-Bernoulli update. Our approach bases on the Bayes Parallel Combination Rule and can be seen as multi-sensor multi-object Information Matrix Fusion for synchronous sensors, which constitutes a perfect choice in centralized systems with distributed sensors. Finally, we compare the proposed method to the Iterator Corrector approach from literature in detailed simulations.