自适应高阶容积卡尔曼滤波在目标跟踪中的应用

doi:10.7527/S1000-6893.2015.0180

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自适应高阶容积卡尔曼滤波在目标跟踪中的应用

崔乃刚¹, 张龙¹, 王小刚¹, 杨峰¹, 卢宝刚²

1. 哈尔滨工业大学航天学院, 哈尔滨 150001;
2. 北京航天长征飞行器研究所, 北京 100076

收稿日期:2015-01-09 修回日期:2015-06-14 出版日期:2015-12-15 发布日期:2015-07-02
通讯作者: 崔乃刚,Tel.:0451-86412766,E-mail:Cui_Naigang@163.com E-mail:Cui_Naigang@163.com
作者简介:崔乃刚,男,博士,教授,博士生导师。主要研究方向:滤波理论及应用,导弹飞行力学与控制,组合导航及自主导航技术,惯导系统对准技术,导弹攻防对抗仿真。Tel:0451-86412766,E-mail:Cui_Naigang@163.com;张龙,男,博士研究生。主要研究方向:非线性滤波应用,目标跟踪,传感器数据融合,飞行器制导与控制,E-mail:zhanglong_hit@163.com
基金资助:
国家自然科学基金(61304236)

Application of adaptive high-degree cubature Kalman filter in target tracking

CUI Naigang¹, ZHANG Long¹, WANG Xiaogang¹, YANG Feng¹, LU Baogang²

1. Department of Astronautics Engineering, Harbin Institute of Technology, Harbin 150001, China;
2. Beijing Institute of Space Long March Vehicle, Beijing 100076, China

Received:2015-01-09 Revised:2015-06-14 Online:2015-12-15 Published:2015-07-02
Supported by:
National Natural Science Foundation of China (61304236)

摘要/Abstract

摘要：

针对传统容积卡尔曼滤波(CKF)在系统状态发生突变时估计精度下降的问题,将强跟踪滤波(STF)算法与高阶容积卡尔曼滤波(HCKF)算法相结合,提出了一种自适应高阶容积卡尔曼滤波(AHCKF)方法。该算法采用高阶球面-相径容积规则,可获得高于传统CKF的估计精度,同时在HCKF算法中引入STF,通过渐消因子在线修正预测误差协方差阵,强迫残差序列正交,提高了算法的鲁棒性,增强了算法应对系统状态突变等不确定因素的能力。将提出的AHCKF算法应用于具有状态突变的机动目标跟踪问题并进行数值仿真,仿真结果表明,AHCKF算法在系统状态发生突变的情况下表现出良好的滤波性能,有效地避免了状态突变造成的滤波精度下降,较传统的CKF、HCKF、交互式多模型-容积滤波(IMM-CKF)和自适应容积卡尔曼滤波(ACKF)算法有更强的鲁棒性和系统自适应能力。

关键词: 非线性估计, 目标跟踪, 自适应滤波, 容积卡尔曼滤波, 高阶球面-相径容积规则

Abstract:

An improved high-degree cubature Kalman filter(HCKF)combined with strong tracking filter (STF) algorithm is proposed to overcome the decreasing estimation precision of conventional cubature Kalman filter (CKF) when system states suddenly change and an adaptive high-degree cubature Kalman filter (AHCKF) is established. Based on the high-degree spherical-radial cubature rule, the new method can obtain better accuracy than conventional CKF. Meanwhile, by introducing the STF into HCKF and modifying the predicted states' error covariance with a fading factor, the residual sequence is forced to be orthogonal so that the robustness of the filter and the capability to deal with uncertainty factors are improved. A maneuvering target tracking problem with unknown sudden states' changes in system states is used to test the performance of the proposed filter. The simulation results indicate that AHCKF can achieve good filtering performance and effectively overcome the precision decrease of filters when states' changes suddenly occur, with greater robustness and better system adaptive capacity than the conventional CKF, HCKF, interacting multiple mode-based CKF (IMM-CKF) and adaptive cubature Kalman filter (ACKF).

Key words: nonlinear estimation, target tracking, adaptive filtering, cubature Kalman filtering, high-degree spherical-radial cubature rule

中图分类号:

V557

崔乃刚, 张龙, 王小刚, 杨峰, 卢宝刚. 自适应高阶容积卡尔曼滤波在目标跟踪中的应用[J]. 航空学报, 2015, 36(12): 3885-3895.

CUI Naigang, ZHANG Long, WANG Xiaogang, YANG Feng, LU Baogang. Application of adaptive high-degree cubature Kalman filter in target tracking[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015, 36(12): 3885-3895.

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E-mail：hkxb@buaa.edu.cn

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自适应高阶容积卡尔曼滤波在目标跟踪中的应用

Application of adaptive high-degree cubature Kalman filter in target tracking

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