The particle filter based on random number searching algorithm for parameter estimation. Issue 2 (7th February 2017)

• Record Type: Journal Article
• Authors: Zheng, Wei; Han, Juan; Kong, Weijie; Ren, Dewang
• Is Part Of: Communications in statistics. Volume 46:Issue 2(2017)
• Page Start: 1401
• Page End: 1413
• Publication Date: 2017-02-07
• Subjects: Linear Gaussian system -- Parameter estimation -- Particle filter algorithm -- Random number searching algorithm; 62F10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1004269
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This article addresses the issue of parameter estimation in linear system in the presence of Gaussian noises, under which the random number searching algorithm (LJ (Luus and Jaakola) algorithm) is combined with the Rao-Blackwellised particle filter (RBPF) algorithm. This yields the so-called RBPF algorithm based on LJ (RBPF-LJ). Unlike the mature alternatives of generic particle filter, the parameter particles of RBPF-LJ are set as random numbers that search in the parameter value scope, which is regulated based on the estimation result to track the changes of the unknown parameter. The contrasting simulations show that the proposed RBPF-LJ outperform the RBPF as well as the particle filter based on kernel smoothing contraction algorithm on the estimation of the dynamically linear or nonlinear parameter and it can obtain the similar estimation results on the static parameter if some coefficients are regulated.