Improved Divided Difference Filter based on Newton-Raphson Method for Target Tracking

被引：0

作者：

Shi, Yong ^{[1
]}

Han, Chongzhao ^{[1
]}

Liang, Yongqi ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Elect & Informat Engn Dept, Xian 710049, Shaan Xi Prov, Peoples R China

来源：

FUSION: 2009 12TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4 | 2009年

关键词：

Tracking; Nonlinear state estimation; divided difference filter; Newton-Raphson method; NONLINEAR ESTIMATION; STATE ESTIMATION; SYSTEMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, improved divided difference filter, which will be called IDDF for brevity, is proposed for tat-get tracking with nonlinear observation models. The new algorithm is derived from the Newton-Raphson method (or Newtons method) to approximate maximum a posterior (MAP) estimation. We demonstrate the direct and intuitive relationship between the iterated extended Kalman filter and Newton-Raphson method and can extend the divided difference filter so that iteration is possible. Simulation results show that the proposed filter provides better performance in tracking accuracy when compared to standard DDF, iterated extended Kalman filter (IEKF) and extended Kalman filter (EKF) in presence of severe nonlinearity.

引用

页码：2068 / 2074

页数：7