Comparison of recursive estimation techniques for position tracking radioactive sources

Abstract

Los Alamos NATIONAL LABORATORY Los Alamos National Laboratory, an affirmative actionlequal opportunityemployer, is operated by the University of California for the U.S. Department of Energy under mntract W-7405-ENG-36. Byaccaptance of this aticle, the publisher recognizes that the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution,or to allow othera to do so, for U.S. Government purposes. Los Alamos National Laboratow requests that the publisher identifythis article as work performed under the auspices of the U.S. Department of Energy. Los Alamos National Laboratow strongly suppofi academic freedom and a researcher's rightto publish;as an institution,however, the Laboratory does not endorse the viewpoint of a publicationorguamntee its technical correctness.

Airborne radiation mapping: overview and application of current and future aerial systems

“…However, the algorithm still struggles with highly non-linear processes, as is experienced at lower levels of radiation intensity. As a result, the accuracy of this method is reported to be lower than other methods, particularly MLE algorithms and more complicated RBE filters (GF, EF, and PF) (Brewer 2009;Gunatilaka, Ristic, and Gailis 2007;Morelande, Ristic, and Gunatilaka 2007;Muske and Howse 2001;Towler, Krawiec, and Kochersberger 2012).…”

“…Whilst each of the individual algorithms differ in terms of the specific statistical models used or the assumptions made with respect to the initial model conditions (i.e. assumption of a known source radionuclide or intensity), each is based around the Poisson distribution of radioactive decay and emission (Brennan, Mielke, and Torney 2005;Knoll 2010;Muske and Howse 2001;Towler, Krawiec, and Kochersberger 2012). At high radiation intensities, the Poisson distribution equates well to a Gaussian distribution (Brennan, Mielke, and Torney 2005;Towler, Krawiec, and Kochersberger 2012).…”

“…A number of studies have explored the use of RBE algorithms in localizing radiation sources (Brennan, Mielke, and Torney 2005;Brewer 2009;Gunatilaka, Ristic, and Gailis 2007;Morelande, Ristic, and Gunatilaka 2007;Muske and Howse 2001;Towler, Krawiec, and Kochersberger 2012). This type of algorithm operates by predicting the behaviour of a system based upon a predefined process model and refining this prediction as new measurements become available.…”

“…The reviewed algorithms include recursive non-linear least squares optimization algorithms (Howse, Ticknor, and Muske 2001), several variations on recursive Bayesian estimation (RBE) algorithms (Brennan, Mielke, and Torney 2005;Brewer 2009;Morelande, Ristic, and Gunatilaka 2007;Muske and Howse 2001;Towler, Krawiec, and Kochersberger 2012), maximum likelihood estimator (MLE) algorithms (Gunatilaka, Ristic, and Gailis 2007;Morelande, Ristic, and Gunatilaka 2007) and mapping-based localization techniques (Towler, Krawiec, and Kochersberger 2012) 4.2.1. Recursive non-linear least squares optimization algorithms Work conducted within Howse, Ticknor, and Muske (2001, 1727-1737) utilized a nonlinear least squares optimization algorithm to track a single moving source within a room in real time.…”

Radiation Mapping in Post-Disaster Environments Using an Autonomous Helicopter

“…It is tempting simply to attempt to linearize the model in the style of the extended Kalman filter (EKF), but Muske and Howse found that the nature of the nonlinearities inherent in the model can drive at least the EKF unstable [4]. At least part of this instability results from the highly non-Gaussian shape of the Poisson model that describes radioactive sources at low count rates.…”