Novel approach to nonlinear/non-Gaussian Bayesian state estimation

1993/01/01 by Neil Gordon, N.J. Gordon, David Salmond +3 · 4,401 citations
Computer Science · Engineering · Mathematics · #Advanced Statistical Methods and Models #Fault Detection and Control Systems #Target Tracking and Data Fusion in Sensor Networks

paper · doi:10.1049/ip-f-2.1993.0015

published in IEE Proceedings F Radar and Signal Processing 140(2), 107 (Institution of Engineering and Technology (IET))

crossref issued 1993/01/01 · crossref published 1993/01/01 · crossref published-print 1993/01/01 · openalex publication_date 1993/01/01 · crossref created 2010/06/10 · crossref deposited 2021/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05 · crossref indexed 2026/08/08

Abstract

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.

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