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Abstract: This paper proposes a novel framework for delay-tolerant particle filteringthat is computationally efficient and has limited memory requirements. Withinthis framework the informativeness of a delayed out-of-sequence measurementOOSM is estimated using a lightweight procedure and uninformativemeasurements are immediately discarded. The framework requires theidentification of a threshold that separates informative from uninformative;this threshold selection task is formulated as a constrained optimizationproblem, where the goal is to minimize tracking error whilst controlling thecomputational requirements. We develop an algorithm that provides anapproximate solution for the optimization problem. Simulation experimentsprovide an example where the proposed framework processes less than 40% of allOOSMs with only a small reduction in tracking accuracy.



Autor: Boris N. Oreshkin, Xuan Liu, Mark J. Coates

Fuente: https://arxiv.org/







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