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• W2891864367 abstract "The Bernoulli filter (BF) is a Bayes-optimal method for target tracking when the target can be present or absent in unknown time intervals and the measurements are affected by clutter and missed detections. We propose a distributed particle-based multisensor BF algorithm that approximates the centralized multisensor BF for arbitrary nonlinear and non-Gaussian system models. Our distributed algorithm uses a new extension of the likelihood consensus (LC) scheme that accounts for both target presence and absence and includes an adaptive pruning of the LC expansion coefficients. Simulation results for a heterogeneous sensor network with significant noise and clutter show that the performance of our algorithm is close to that of the centralized multisensor BF." @default.
• W2891864367 created "2018-09-27" @default.
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• W2891864367 date "2018-07-01" @default.
• W2891864367 modified "2023-09-23" @default.
• W2891864367 title "A Distributed Bernoulli Filter Based on Likelihood Consensus with Adaptive Pruning" @default.
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• W2891864367 doi "https://doi.org/10.23919/icif.2018.8455302" @default.
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