A framework for particle filtering in positioning, navigation and tracking problems

被引：0

作者：

Gustafsson, F ^{[1
]}

Gunnarsson, F ^{[1
]}

Bergman, N ^{[1
]}

Forssell, U ^{[1
]}

Jansson, J ^{[1
]}

Nordlund, PJ ^{[1
]}

Karlsson, R ^{[1
]}

机构：

[1] Linkoping Univ, Dept Elect Engn, SE-58183 Linkoping, Sweden

来源：

2001 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING PROCEEDINGS | 2001年

关键词：

D O I：

10.1109/SSP.2001.955215

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A framework for positioning, navigation and tracking problems using particle filters (recursive Monte Carlo methods) is developed. Automotive and airborn applications, approached in this framework, have proven a numerical advantage over classical Kalman filter based algorithms. Here the use of non-linear measurement models and non-Gaussian measurement noise is the main explanation for the improvement in accuracy, and models for relevant sensors are surveyed.

引用

页码：34 / 37

页数：4

共 10 条

[1] [Anonymous], LINEAR ESTIMATION
[2] Bar-Shalom Yaakov., 1993, ESTIMATION TRACKING
[3] BERGMAN N, 1999, THESIS LINKOPING U S
[4] DEFREITAS JFG, 2000, SEQUENTIAL MONTE CAR
[5] On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A
Godsill, S
Andrieu, C
[J]. STATISTICS AND COMPUTING, 2000, 10 (03) : 197 - 208
[6] DRANE C, 1998, IEEE COMMUNICATIONS, V36
[7] NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION
GORDON, NJ
SALMOND, DJ
SMITH, AFM
[J]. IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING, 1993, 140 (02) : 107 - 113
[8] Gustafsson F, 2000, ADAPTIVE FILTERING C
[9] Filtering via simulation: Auxiliary particle filters
Pitt, MK
Shephard, N
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1999, 94 (446) : 590 - 599
[10] Spiegelhalter D., 1995, MARKOV CHAIN MONTE C

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