基于改进集合卡尔曼滤波方法的目标运动轨迹多源异步数据融合方法研究

张泽群; 任文娟; 付琨; 方继飞; 张跃

doi:10.11999/JEIT171115

基于改进集合卡尔曼滤波方法的目标运动轨迹多源异步数据融合方法研究

doi: 10.11999/JEIT171115

1.
中国科学院电子学研究所北京 100190
2.
中国科学院空间信息处理与应用系统技术重点实验室北京 100190

详细信息

作者简介:
张泽群：男，1990 年生，助理研究员，研究方向为反问题、模型优化、地理空间信息挖掘

任文娟：女，1982 年生，副研究员，研究方向为电子目标识别、数据挖掘

付琨：男，1976 年生，研究员，研究方向为计算机视觉与遥感图像理解、地理空间信息挖掘与可视化

方继飞：男，1989 年生，工程师，研究方向为轨迹分析

张跃：男，1990 年生，助理研究员，研究方向为计算机视觉与遥感图像理解

通讯作者:
张泽群　 zqzhang1@mail.ie.ac.cn

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2017-11-28
- 修回日期: 2018-05-25
- 网络出版日期: 2018-07-12
- 刊出日期: 2018-09-01

Research on Multi-source and Asynchronous Data Fusion of Target Trajectory Based on the Modified Ensemble Kalman Filter Method

1.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2.
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Science, Beijing 100190, China

摘要

摘要: 该文构建了一个改进的多源异步观测数据情景下基于非线性运动学本构方程的集合卡尔曼滤波理论模型，该模型可以精确反演出目标运动状态参数(速度、加速度)以对目标后续运动进行预测。并基于集合卡尔曼滤波实现了多源观测数据融合，利用高精度观测数据修正低精度观测数据，修正后的数据精度可通过集合卡尔曼滤波提供的统计学信息进行标定，为非线性情形下目标轨迹多源异步数据融合问题提供了新的解决思路。
- 数据融合 /
- 目标轨迹分析 /
- 集合卡尔曼滤波
Abstract: A modified Ensemble Kalman Filter (EnKF) theory model based on kinematic equations is proposed to realize the historical fitting analysis and trajectory prediction of the target trajectory in the multi-source observation data scenario. This model is applied to accurately calculate the target motion state parameters (velocity and acceleration), then the target’s follow-up movement is predicted. The multi-source observation data fusion is realized by using the EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF.
- Data fusion /
- Target trajectory analysis /
- Ensemble Kalman Filter (EnKF)

HTML全文

图 1 目标轨迹观测值和观测误差分布

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图 2 初始猜测值(速度、加速度)对目标轨迹的预测

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图 3 集合卡尔曼滤波实现目标轨迹的历史拟合

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图 4 集合卡尔曼滤波实现目标轨迹的历史拟合

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图 5 X方向速度集合卡尔曼滤波反演结果

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图 6 Y方向速度集合卡尔曼滤波反演结果

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图 7 X方向加速度集合卡尔曼滤波反演结果

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图 8 Y方向加速度集合卡尔曼滤波反演结果

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图 9 集合卡尔曼滤波运动拟合结果与卡尔曼滤波、扩展卡尔曼滤波和粒子滤波对比

下载: 全尺寸图片幻灯片

表 1 目标轨迹观测值

时刻(h)	X轴(km)	Y轴(km)
1	5.1	3.9
3	15.9	11.1
5	27.5	17.5
8	43.4	25.6
10	63.0	33.0
14	89.6	36.4
18	122.4	39.6
20	144.0	44.0
21	149.1	39.9
25	187.5	37.5
29	229.1	34.9
32	262.4	21.6
35	297.5	17.5
39	347.1	3.9
41	373.1	–4.1

下载: 导出CSV

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