NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION

被引：4784

作者：

GORDON, NJ ^{[1
]}

SALMOND, DJ ^{[1
]}

SMITH, AFM ^{[1
]}

机构：

[1] UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED,DEPT STAT,LONDON SW7 2AZ,ENGLAND

来源：

IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING | 1993年 / 140卷 / 02期

关键词：

KALMAN FILTER; SEQUENTIAL ESTIMATION; BAYESIAN FILTER;

D O I：

10.1049/ip-f-2.1993.0015

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

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.

引用

页码：107 / 113

页数：7

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