Multiple-Model Bernoulli Filters-Part I: A Gaussian Mixture Implementation

被引：0

作者：

Jiang, Tongyang ^{[1
,2
]}

机构：

[1] China Elect Technol Grp Corp, Res Inst 38, Hefei 230088, Peoples R China

[2] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

来源：

PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年

关键词：

Random finite set; Bernoulli filtering; multiple-model; Gaussian mixture; linear Gaussian models; JOINT DETECTION; TRACKING;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Bernoulli filter is an exact Bayesian filter for target tracking under the framework of random finite sets. It has been implemented by using Gaussian mixture (GM) and sequential Monte Carlo (SMC) techniques for joint target detection and tracking. However, a single model is not enough to accommodate a maneuvering target that switches between a set of motion models. In this paper, we extend the Bernoulli filter to switching motion models, and propose a multiple-model (MM) Bernoulli filter for a maneuvering target. Then we give a GM implementation of the MM Bernoulli filter for linear Gaussian models. Finally, a numerical example is presented to verify the effectiveness of the GM-MM Bernoulli filter for a maneuvering target.

引用

页码：4819 / 4824

页数：6