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Petar M. Djuric

  1. Likelihood Consensus-Based Distributed Particle Filtering with Distributed Proposal Density Adaptation.

    Чт, 09/29/2011 - 15:01 — SomNambul
    Authors: Franz Hlawatsch, Ondrej Hlinka, Petar M. Djuric
    Subjects: Applications
    Abstract

    We present a consensus-based distributed particle filter (PF) for wireless
    sensor networks. Each sensor runs a local PF to compute a global state estimate
    that takes into account the measurements of all sensors. The local PFs use the
    joint (all-sensors) likelihood function, which is calculated in a distributed
    way by a novel generalization of the likelihood consensus scheme. A performance
    improvement (or a reduction of the required number of particles) is achieved by
    a novel distributed, consensus-based method for adapting the proposal densities
    of the local PFs.

  2. Likelihood Consensus and Its Application to Distributed Particle Filtering.

    Чт, 09/01/2011 - 15:01 — SomNambul
    Authors: Franz Hlawatsch, Ondrej Hlinka, Ondrej Sluciak, Petar M. Djuric, Markus Rupp
    Subjects: Applications
    Abstract

    We consider distributed state estimation in a wireless sensor network without
    a fusion center. Each sensor performs a global estimation task - based on the
    past and current measurements of all sensors - using only local processing and
    local communications with its neighbors. In this task, the joint (all-sensors)
    likelihood function (JLF) plays a central role as it epitomizes the
    measurements of all sensors. We propose a distributed method for computing an
    approximation of the JLF by means of consensus algorithms.

Math-arch

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