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
收藏
页码：34 / 37
页数：4
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