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|本期目录/Table of Contents|

两阶段卡尔曼滤波自适应交互式多模型算法

《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

期数:
2010年03期
页码:
83
栏目:
出版日期:
2010-06-26

文章信息/Info

Title:
Adaptive Interactive Multiple Model of Two-stage Kalman Filter Algorithm
文章编号:
1008-1194(2010)03-0083-04
作者:
廖阳1; 王睿1; 曾昭博1; 2; 熊加遥1
1.空军工程大学导弹学院，陕西 三原713800；2.93617部队，北京101400
Author(s):
LIAO Yang1; WANG Rui1; ZENG Zhaobo1; 2; XIONG Jiayao1
1.The Missile Institute,Air Force Engineering University,Sanyuan 713800,China;2.N0.93617 Unit of PLA,
Beijing 101400,China
关键词:
机动目标跟踪; 卡尔曼滤波; 交互式多模型(IMM)
Keywords:
maneuvering target tracking; Kalman filter; interactive multiple model algorithm(IMM)
分类号:
TN953
DOI:
.
文献标识码:
A
摘要:
对于机动目标跟踪问题，由于目标机动能力的增强，需建立大量模型来逼近真实模式，使建立的目标模型与目标的实际运动适配，但这使计算量增大，而且性能不一定能提高。针对这个问题，将两阶段卡尔曼滤波器与一般的交互式多模型算法相结合，设计了一种自适应交互式多模型算法。该算法采用两阶段卡尔曼估计器估计目标的加速度，然后将其反馈到由多个不同参数构成子滤波器的交互式多模型滤波算法中进行交互式多模型滤波。与自适应半交互式多模型算法进行对比的仿真验证了该算法有效地减少了子滤波器的数量，同时在一定程度上也提高了跟踪的精度。
Abstract:
Maneuvering targets tracking needs large amounts of models to approach real targets.An adaptive interactive multiple model algorithm with combing the two-stage Kalman filter and the general interactive multiple model algorithm was proposed in this paper.The proposed algorithm estimated the acceleration of the target based on the two-stage Kalman filter,which is feedback to the interactive multiple model algorithm.The simulation based on the comparison between the proposed method and a half adaptive interactive multiple model algorithm further proved the validity of this algorithm.

参考文献/References

［1］Bar-Shalom Y,Blair W D.Multitarget-multisensor tracking: applications and advances［M］.Boston:Artech House,2000.
［2］Li X R.Multiple-model estimation with variable structure-part Ⅱ:model-set adaptation［J］.IEEE Transactions on Automatic Control,2000,45 (11):2 047-2 060.
［3］Daeipour E,Bar-Shalom Y.An interacting model approach for target with Glient noise［J］.IEEE Transactions on Aerospace and Electronics,1995,31(2):706-716.
［4］L A Johnston,V Krishnamurthy.An improvement to the interacting multiple model (IMM) algorithm［J］.IEEE Transactions on Signal Processing,2001,49(12):2 909-2 923.
［5］Alouani A T, Xia P,Rice T R,et al.A two-stage Kalman estimator for state estimation in the presence of random bias and for tracking maneuvering targets［C］//Proceedings of 30th IEEE Conference on Decision and control.New York:IEEE,1991:2 059-2 062.
［6］Alouani A T,Xia P,Rice T R,Blar W D On the Optimality of Two-Stage State Estimation in the presence of Random Bias［C］//IEEE Trans on Automat. Contr.US:IEEE,1993 AC:1 279-1 282
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备注/Memo

备注/Memo:
*收稿日期：2009-11-21    修回日期：2010-02-06
作者简介：廖阳(1985-)，男，陕西宝鸡人，硕士研究生，研究方向：信号与信息处理。E-mail：liaoyang5258@qq.com。
更新日期/Last Update: 2010-07-13