COMPUTATION AND VISUALIZATION OF POSTERIOR DENSITIES IN SCALAR NONLINEAR AND NON-GAUSSIAN BAYESIAN FILTERING AND SMOOTHING PROBLEMS

被引：0

作者：

Roth, Michael ^{[1
]}

Gustafsson, Fredrik ^{[1
]}

机构：

[1] Linkoping Univ, Dept Elect Engn, Linkoping, Sweden

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年

基金：

瑞典研究理事会;

关键词：

Bayesian filtering; smoothing; point mass filter; particle filter; PARTICLE FILTERS;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper.

引用

页码：4686 / 4690

页数：5

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