Practical application of a branching particle-based nonlinear filter

David J. Ballantyne; John R. Hoffman; Michael A. Kouritzin

doi:10.1117/12.436953

16 August 2001 Practical application of a branching particle-based nonlinear filter

David J. Ballantyne, John R. Hoffman, Michael A. Kouritzin

Proceedings Volume 4380, Signal Processing, Sensor Fusion, and Target Recognition X; (2001) https://doi.org/10.1117/12.436953
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

Particle-based nonlinear filters provide a mathematically optimal (in the limit) and sound method for solving a number of difficult filtering problems. However, there are a number of practical difficulties that can occur when applying particle-based filtering techniques to real world problems. These problems include highly directed signal dynamics highly definitive observations clipped observation data. Current approaches to solving these problems generally require increasing the number of particles, but to obtain a given level of performance the number of particles required may be extremely large. We propose a number of techniques to ameliorate these difficulties. We adopt the ideas of simulated annealing and add noise which is damped in time to the particle states when they are evolved or duplicated, and also add noise which is damped in time to the interpretation of the observations by the filter, to deal with signal dynamics and observation problems. We modify the method by which particles are duplicated to deal with different information flows into the system depending on the location of the particle and the information flow into the particle. We discuss the success we have had with these solutions on some of the problems of interest to Lockheed Martin and the MITACS-PINTS research center.

Citation Download Citation

David J. Ballantyne, John R. Hoffman, and Michael A. Kouritzin "Practical application of a branching particle-based nonlinear filter", Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); https://doi.org/10.1117/12.436953

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