Novel approach to nonlinear/non-Gaussian Bayesian state estimation

1993/01/01 by Neil Gordon, David Salmond, A. F. M. Smith · 21 citations
Computer Science · Mathematics · Engineering · #Target Tracking and Data Fusion in Sensor Networks #Advanced Statistical Methods and Models #Fault Detection and Control Systems

paper · doi:10.1049/ip-f-2.1993.0015

openalex publication_date 1993/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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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