Sigma Point Belief Propagation

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 propagation (BP) message passing scheme. SPBP achieves approximate marginalizations of posterior distributions corresponding to (generally) loopy factor graphs. It is well suited for decentralized inference because of its low communication requirements. For a decentralized, dynamic sensor localization problem, we demonstrate that SPBP can outperform nonparametric (particle-based) BP while requiring significantly less computations and communications.Comment: 5 pages, 1 figur

Mapping atomistic to coarse-grained polymer models using automatic simplex optimization to fit structural properties

We develop coarse-grained force fields for poly (vinyl alcohol) and poly (acrylic acid) oligomers. In both cases, one monomer is mapped onto a coarse-grained bead. The new force fields are designed to match structural properties such as radial distribution functions of various kinds derived by atomistic simulations of these polymers. The mapping is therefore constructed in a way to take into account as much atomistic information as possible. On the technical side, our approach consists of a simplex algorithm which is used to optimize automatically non-bonded parameters as well as bonded parameters. Besides their similar conformation (only the functional side group differs), poly (acrylic acid) was chosen to be in aqueous solution in contrast to a poly (vinyl alcohol) melt. For poly (vinyl alcohol) a non-optimized bond angle potential turns out to be sufficient in connection with a special, optimized non-bonded potential. No torsional potential has to be applied here. For poly (acrylic acid), we show that each peak of the radial distribution function is usually dominated by some specific model parameter(s). Optimization of the bond angle parameters is essential. The coarse-grained forcefield reproduces the radius of gyration of the atomistic model. As a first application, we use the force field to simulate longer chains and compare the hydrodynamic radius with experimental data.Comment: 34 pages, 3 tables, 16 figure

Mapping atomistic to coarse-grained polymer models using automatic simplex optimization to fit structural properties

We develop coarse-grained force fields for poly (vinyl alcohol) and poly (acrylic acid) oligomers. In both cases, one monomer is mapped onto a coarse-grained bead. The new force fields are designed to match structural properties such as radial distribution functions of various kinds derived by atomistic simulations of these polymers. The mapping is therefore constructed in a way to take into account as much atomistic information as possible. On the technical side, our approach consists of a simplex algorithm which is used to optimize automatically non-bonded parameters as well as bonded parameters. Besides their similar conformation (only the functional side group differs), poly (acrylic acid) was chosen to be in aqueous solution in contrast to a poly (vinyl alcohol) melt. For poly (vinyl alcohol) a non-optimized bond angle potential turns out to be sufficient in connection with a special, optimized non-bonded potential. No torsional potential has to be applied here. For poly (acrylic acid), we show that each peak of the radial distribution function is usually dominated by some specific model parameter(s). Optimization of the bond angle parameters is essential. The coarse-grained forcefield reproduces the radius of gyration of the atomistic model. As a first application, we use the force field to simulate longer chains and compare the hydrodynamic radius with experimental data.Comment: 34 pages, 3 tables, 16 figure

Distributed Estimation with Information-Seeking Control in Agent Network

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.Comment: 17 pages, 10 figure

Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus

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. Starting from a factor graph formulation of the CoSLAT problem, we develop a particle-based, distributed message passing algorithm for CoSLAT that combines nonparametric belief propagation with the likelihood consensus scheme. The proposed CoSLAT algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging probabilistic information between CSL and DTT. Simulation results demonstrate substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE Transaction on Signal Processin