Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Viscous layer meshes from level sets on cartesian meshes

The long term goal of our work is to enable rapid prototyping design optimization to take place on geometries of arbitrary size in a spirit of a real time computer game. In recent papers we have reported the integration of a Level Set based geometry kernel with an octree-based cut-Cartesian mesh generator, RANS flow solver and post-processing all within a single piece of software - and all implemented in parallel with commodity PC clusters as the target. This work has shown that it is possible to eliminate all serial bottlenecks from the CED Process. This paper reports further progress towards our goal; in particular we report on the generation of viscous layer meshes to bridge the body to the flow across the cut-cells. The Level Set formulation, which underpins the geometry representation, is used as a natural mechanism to allow rapid construction of conformal layer meshes. The guiding principle is to construct the mesh which most closely approximates the body but remains solvable. This apparently novel approach is described and examples given.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.