A hybrid neural network-based IE and IMM architecture for target tracking

被引：2

作者：

Jian, Rong ^{[1
]}

Xiu, Wang ^{[1
]}

Xiaochun, Zhong ^{[2
]}

Haitao, Zhang ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Phys Elect, Chengdu 610054, Peoples R China

[2] Southwest Jiaotong Univ, Dept Appl Phys, Chengdu 610031, Peoples R China

来源：

2008 WORKSHOP ON POWER ELECTRONICS AND INTELLIGENT TRANSPORTATION SYSTEM, PROCEEDINGS | 2008年

基金：

中国国家自然科学基金;

关键词：

IMM; Neural network-based IE; Target Tracking;

D O I：

10.1109/PEITS.2008.28

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In order to enable a tracking system to work stably in the environment with,fast maneuver and rapidly changing noise, a new hybrid architecture combining Interacting Multiple Model (IMM) and neural network-based Input estimate (IE) together is presented in this paper. In this architecture, IMM provides estimation of covariance of measurement noise to neural network-based IE, while IE enables the system to work effectively when the targets lead fast and complex maneuver, both of the outputs of IMM and NNIE will be fused in, fusion module. In order to verb the effectiveness of this architecture, several simulations were leaded and the results prove it can work stably with rapidly changing noise and fast maneuver.

引用

页码：214 / +

页数：2