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.

A metal-polymer composite with unusual properties

Electrically conductive composites that contain conductive filler dispersed in an insulating polymer matrix are usually prepared by the vigorous mixing of the components. This affects the structure of the filler particles and thereby the properties of the composite. It is shown that by careful mixing nano-scale features on the surface of the filler particles can be retained. The fillers used possess sharp surface protrusions similar to the tips used in scanning tunnelling microscopy. The electric field strength at these tips is very large and results in field assisted (Fowler-Nordheim) tunnelling. In addition the polymer matrix intimately coats the filler particles and the particles do not come into direct physical contact. This prevents the formation of chains of filler particles in close contact as the filler content increases. In consequence the composite has an extremely high resistance even at filler loadings above the expected percolation threshold. The retention of filler particle morphology and the presence of an insulating polymer layer between them endow the composite with a number of unusual properties. These are presented here together with appropriate physical models. © 2005 IOP Publishing Ltd.

A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models

In this article, we develop a new Rao-Blackwellized Monte Carlo smoothing algorithm for conditionally linear Gaussian models. The algorithm is based on the forward-filtering backward-simulation Monte Carlo smoother concept and performs the backward simulation directly in the marginal space of the non-Gaussian state component while treating the linear part analytically. Unlike the previously proposed backward-simulation based Rao-Blackwellized smoothing approaches, it does not require sampling of the Gaussian state component and is also able to overcome certain normalization problems of two-filter smoother based approaches. The performance of the algorithm is illustrated in a simulated application. © 2012 IFAC.