Ondrej Hlinka

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Distributed particle filter (DPF) algorithms are sequential state estimation algorithms that are executed by a set of agents. Some or all of the agents perform local particle filtering and interact with other agents to calculate a global state estimate. DPF algorithms are attractive for large-scale, nonlinear, and non-Gaussian distributed estimation(More)
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 estimation task, the joint (all-sensors) likelihood function (JLF) plays a(More)
ITS2 sequences are used extensively in molecular taxonomy and population genetics of arthropods and other animals yet little is known about the molecular evolution of ITS2. We studied the secondary structure of ITS2 in species from each of the six main lineages of hard ticks (family Ixodidae). The ITS2 of these ticks varied in length from 679 bp in Ixodes(More)
We propose a distributed implementation of the Gaussian particle filter (GPF) for use in a wireless sensor network. Each sensor runs a local GPF that computes a global state estimate. The updating of the particle weights at each sensor uses the joint likelihood function, which is calculated in a distributed way, using only local communications, via the(More)
We present a distributed particle filtering scheme for time-space-sequential Bayesian state estimation in wireless sensor networks. Low-rate inter-sensor communications between neighboring sensors are achieved by transmitting Gaussian mixture (GM) representations instead of particles. The GM representations are calculated using a clustering algorithm. We(More)
We propose an iterative extension of the covariance intersection (CI) algorithm for distributed data fusion. Our iterative CI (ICI) algorithm is able to disseminate local information throughout the network. We show that the ICI algorithm converges asymptotically to a consensus across all network nodes. We furthermore apply the ICI algorithm to distributed(More)
We develop a distributed particle filter for sequential estimation of a global state in a decentralized wireless sensor network. A global state estimate that takes into account the measurements of all sensors is computed in a distributed manner, using only local calculations at the individual sensors and local communication between neighboring sensors. The(More)
The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief(More)
We propose a distributed method for computing the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. A consensus algorithm is used for a decentralized, iterative calculation of a sufficient statistic that describes an approximation to the JLF. After convergence of the consensus algorithm, the approximate JLF—which epitomizes the(More)
We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor measurements.(More)