Research on Pseudo-Linear Kalman Filter and Extended Kalman Filter Passive Location Algorithm

曾钰; 林都

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Research on Pseudo-Linear Kalman Filter and Extended Kalman Filter Passive Location Algorithm

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- Research on Pseudo-Linear Kalman Filter and Extended Kalman Filter Passive Location Algorithm
- 基于伪线性KF和EKF的无源定位算法研究
- Vol. 32, Issue 2, Pages: 174-178(2011)
- 作者机构：
  
  中北大学信息与通信工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
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曾钰, 林都. Research on Pseudo-Linear Kalman Filter and Extended Kalman Filter Passive Location Algorithm[J]. 2011, 32(2): 174-178.
DOI：

曾钰, 林都. Research on Pseudo-Linear Kalman Filter and Extended Kalman Filter Passive Location Algorithm[J]. 2011, 32(2): 174-178. DOI：

摘要

针对扩展卡尔曼滤波(Extended Kalman Filter

EKF)对非线性观测模型观测时

滤波曲线易出现发散的问题

采用伪线性卡尔曼滤波(Pseudo-Linear Kalman Filter

PLKF)方法对非线性观测方程进行了分析.通过将非线性因素整合到观测矩阵中

实现了非线性观测方程的伪线性化

同时与两站点EKF算法进行了特性比较.通过Monte Carlo仿真得到了滤波误差

结果表明:对非线性观测模型定位时

采用单站伪线性KF算法

在x

z方向的滤波时间均在20个周期以内

均方根误差(Root Mean Square Error

RMSE)在0-1之间;而两站点EKF算法的滤波时间则在25~30个周期之间

均方根误差在1以上.

Abstract

The curve of extended Kalman filter(EKF) for nonlinear observation model is susceptible to being divergent

so pseudo-linear Kalman filter(Pseudo-Linear KF) was used to analysis the nonlinear equations.The nonlinear equations were pseudo-linearized by coordinating the nonlinear factors to observing matrixes.The above work was compared with the two-stations EKF in characteristics.The filtering errors were obtained thorough Monte Carlo simulation.The results showed that for nonlinear observation model

the filtering time of one-station Pseudo-Linear KF was lower than 20 periods

and the root mean square error(RMSE) was greater than 0 and less than 1 in x

y and z direction

but the filtering time of two-stations EKF was more than 25 periods and less than 30 periods

and the root mean square error was greater than 1.

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references

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