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This paper proposes a novel framework for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. Within this framework the informativeness of a delayed (out-of-sequence) measurement (OOSM) is estimated using a lightweight procedure and uninformative measurements are immediately discarded. The framework requires the identification of a threshold that...
We present a distributed particle filtering algorithm for target tracking in sensor networks. Several existing algorithms rely on the establishment and maintenance of a spanning path or tree. This is challenging in networks with dynamic topologies induced by mobile nodes and changing wireless conditions; the algorithms are vulnerable to link or node failure. More recent algorithms employ consensus...
This paper proposes a novel algorithm for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. The algorithm estimates the informativeness of delayed (out-of-sequence) measurements (OOSMs) and immediately discards uninformative measurements. More informative measurements are then processed using the storage efficient particle filter proposed by Orguner...
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