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- Ondrej Hlinka, Ondrej Sluciak, Franz Hlawatsch, Petar M. Djuric, Markus Rupp
- IEEE Transactions on Signal Processing
- 2012

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)

- Ondrej Hlinka, Franz Hlawatsch, Petar M. Djuric
- IEEE Signal Processing Magazine
- 2013

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 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)

- Ondrej Hlinka, Ondrej Sluciak, Franz Hlawatsch, Petar M. Djuric, Markus Rupp
- 2011 IEEE International Conference on Acoustics…
- 2011

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 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)

- Florian Meyer, Erwin Riegler, Ondrej Hlinka, Franz Hlawatsch
- ACSCC
- 2012

—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… (More)

- Ondrej Hlinka, Franz Hlawatsch, Petar M. Djuric
- 2012 IEEE International Conference on Acoustics…
- 2012

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… (More)

- Ondrej Hlinka, Franz Hlawatsch, Petar M. Djuric
- IEEE Transactions on Signal Processing
- 2014

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)

- Ondrej Sluciak, Ondrej Hlinka, Markus Rupp, Franz Hlawatsch, Petar M. Djuric
- ACSCC
- 2011

—We propose a sequential likelihood consensus (SLC) for a distributed, sequential computation of the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. The SLC is based on a novel dynamic consensus algorithm, of which only a single iteration is performed per time step. We demonstrate the application of the SLC in a distributed… (More)

- Ondrej Hlinka, Franz Hlawatsch
- 2009 IEEE International Conference on Acoustics…
- 2009

We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial sensor network without a fusion center. We present an optimal distributed Bayesian estimation algorithm that is sequential both in time and in space (i.e., across sensors) and… (More)