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To address the high computational complexity and data association challenges in multi-sensor networks, this paper proposes a spatial-adaptive confidence-weighted Poisson multi-Bernoulli mixture (SACW-PMBM) algorithm that applies a divide-and-conquer fusion strategy.
This is the first architecture to re-conceptualize the complete PMBM filter recursion—including prediction, measurement processing, and the notoriously non-differentiable data association step—as a fully differentiable computational graph.
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