Robust filtering and smoothing with gaussian processes

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail. © 2011 IEEE.

Stainless steel sandwich

The Cambridge University's Gordon Laboratory, in collaboration with Fibertech and the Defence Science and Technology Laboratory in the UK, has developed a novel melt spun fiber bore called 'Fibrecore', fabricated entirely from stainless steel with thin faceplates. Fibrecore is typically manufactured by 5mm-long and 70μm thick stainless steel fibers, produced by a melt overflow process. Its entirely metallic construction allows spot welding and tungsten inert gas welding without difficulty. Fibrecore exhibits different energy absorption mechanisms such as core cushioning, core-faceplate delamination, and plastic faceplate deformation, often in a concertina-like fashion. Its low-cost, high structural efficiency and good energy absorption characteristics make it attractive for a range of commercial and military applications. Such applications being evaluated include vehicle body panels, exhaust system noise reduction, low cost filters, and lightweight physical protection. In addition to these characteristics, Fibrecore exhibits properties such as corrosion protection, vibrational damping, and thermal insulation, which also extend its applications.

Enhanced poisson sum representation for alpha-stable processes

In this paper we present Poisson sum series representations for α-stable (αS) random variables and a-stable processes, in particular concentrating on continuous-time autoregressive (CAR) models driven by α-stable Lévy processes. Our representations aim to provide a conditionally Gaussian framework, which will allow parameter estimation using Rao-Blackwellised versions of state of the art Bayesian computational methods such as particle filters and Markov chain Monte Carlo (MCMC). To overcome the issues due to truncation of the series, novel residual approximations are developed. Simulations demonstrate the potential of these Poisson sum representations for inference in otherwise intractable α-stable models. © 2011 IEEE.

Simultaneous reduction of particulate matter and NOx emissions using 4-way catalyzed filtration systems

The next generation of diesel emission control devices includes 4-way catalyzed filtration systems (4WCFS) consisting of both NOx and diesel particulate matter (DPM) control. A methodology was developed to simultaneously evaluate the NOx and DPM control performance of miniature 4WCFS made from acicular mullite, an advanced ceramic material (ACM), that were challenged with diesel exhaust. The impact of catalyst loading and substrate porosity on catalytic performance of the NOx trap was evaluated. Simultaneously with NOx measurements, the real-time solid particle filtration performance of catalyst-coated standard and high porosity filters was determined for steady-state and regenerative conditions. The use of high porosity ACM 4-way catalyzed filtration systems reduced NOx by 99% and solid and total particulate matter by 95% when averaged over 10 regeneration cycles. A "regeneration cycle" refers to an oxidizing ("lean") exhaust condition followed by a reducing ("rich") exhaust condition resulting in NOx storage and NOx reduction (i.e., trap "regeneration"), respectively. Standard porosity ACM 4-way catalyzed filtration systems reduced NOx by 60-75% and exhibited 99.9% filtration efficiency. The rich/lean cycling used to regenerate the filter had almost no impact on solid particle filtration efficiency but impacted NOx control. Cycling resulted in the formation of very low concentrations of semivolatile nucleation mode particles for some 4WCFS formulations. Overall, 4WCFS show promise for significantly reducing diesel emissions into the atmosphere in a single control device. © 2013 American Chemical Society.

Models and algorithms for tracking of maneuvering objects using variable rate particle filters

Standard algorithms in tracking and other state-space models assume identical and synchronous sampling rates for the state and measurement processes. However, real trajectories of objects are typically characterized by prolonged smooth sections, with sharp, but infrequent, changes. Thus, a more parsimonious representation of a target trajectory may be obtained by direct modeling of maneuver times in the state process, independently from the observation times. This is achieved by assuming the state arrival times to follow a random process, typically specified as Markovian, so that state points may be allocated along the trajectory according to the degree of variation observed. The resulting variable dimension state inference problem is solved by developing an efficient variable rate particle filtering algorithm to recursively update the posterior distribution of the state sequence as new data becomes available. The methodology is quite general and can be applied across many models where dynamic model uncertainty occurs on-line. Specific models are proposed for the dynamics of a moving object under internal forcing, expressed in terms of the intrinsic dynamics of the object. The performance of the algorithms with these dynamical models is demonstrated on several challenging maneuvering target tracking problems in clutter. © 2006 IEEE.