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

Zequn ZHANG; Wenjuan REN; Kun FU; Jifei FANG; Yue ZHANG

doi:10.11999/JEIT171115

Volume 40 Issue 9

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2143-2149

Zequn ZHANG, Wenjuan REN, Kun FU, Jifei FANG, Yue ZHANG. Research on Multi-source and Asynchronous Data Fusion of Target Trajectory Based on the Modified Ensemble Kalman Filter Method[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2143-2149. doi: 10.11999/JEIT171115

Citation:

Zequn ZHANG, Wenjuan REN, Kun FU, Jifei FANG, Yue ZHANG. Research on Multi-source and Asynchronous Data Fusion of Target Trajectory Based on the Modified Ensemble Kalman Filter Method[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2143-2149. doi: 10.11999/JEIT171115

Citation:

Zequn ZHANG, Wenjuan REN, Kun FU, Jifei FANG, Yue ZHANG. Research on Multi-source and Asynchronous Data Fusion of Target Trajectory Based on the Modified Ensemble Kalman Filter Method[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2143-2149. doi: 10.11999/JEIT171115

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

doi: 10.11999/JEIT171115

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

Received Date: 2017-11-28
Rev Recd Date: 2018-05-25

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

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)

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References(17)

References

孙辉, 赵峰, 张峰云. 多传感器信息融合技术及其应用[J]. 海洋测绘, 2009, 29(5): 77–81 doi: 10.3969/j.issn.1671-3044.2009.05.023

SUN Hui, ZHAO Feng, and ZHANG Fengyun. Multisensor information fusion technology and application[J]. Hydrographic Surveying and Charting, 2009, 29(5): 77–81 doi: 10.3969/j.issn.1671-3044.2009.05.023

姜楷娜. 基于卡尔曼滤波的目标轨迹跟踪仿真研究[J]. 中国科技信息, 2017(17): 105–106 doi: 10.3969/j.issn.1001-8972.2017.17.036

JIANG Kaina. Research on target tracking based on Kalman filter[J]. Chian Science and Technology Information, 2017(17): 105–106 doi: 10.3969/j.issn.1001-8972.2017.17.036

王雪君, 孙进平, 张旭旺. 基于压缩感知的PD雷达序贯扩展卡尔曼滤波跟踪方法[J]. 信号处理, 2017, 33(4): 601–606 doi: 10.16798/j.issn.1003-0530.2017.04.022

WANG Xuejun, SUN Jinping, and ZHANG Xuwang. Progressive Extended Kalman Filter Tracking Method Based on Compressive Sensing for PD Radar[J]. Journal of Signal Processing, 2017, 33(4): 601–606 doi: 10.16798/j.issn.1003-0530.2017.04.022

刘晨光, 程丹松, 刘家锋, 等. 一种基于交互式粒子滤波器的视频中多目标跟踪算法[J]. 电子学报, 2011, 39(2): 260–267

LIU Chenguang, CHENG Dansong, LIU Jiafeng, et al. A multi-target tracking algorithm in video based on interactive particle filter[J]. Acta Electronica Sinica, 2011, 39(2): 260–267

陈思静, 张可. VANETs中的车辆移动规律性及轨迹预测研究[J]. 计算机工程与应用, 2016, 52(18): 139–143 doi: 10.3778/j.issn.1002-8331.1410-0313

CHEN Sijing and ZHANG Ke. Research on vehicle movement regularity and trajectory prediction in VANETs[J]. Computer Engineering and Applications, 2016, 52(18): 139–143 doi: 10.3778/j.issn.1002-8331.1410-0313

郭晓军, 万龙, 刘峰. 基于扩展卡尔曼滤波的空间小目标跟踪算法[J]. 电光与控制, 2016, 23(4): 57–61 doi: 10.3969/j.issn.1671-637X.2016.04.012

GUO Xiaojun, WAN Long, and LIU Feng. An algorithm for small space target tracking based on extended Kalman filter[J]. Electronics Optics&Control, 2016, 23(4): 57–61 doi: 10.3969/j.issn.1671-637X.2016.04.012

GELB A and BOOKSX I. Applied optimal estimation[J]. Proceedings of the IEEE, 1974, 64(4): 574–575.

沈凯, 管雪元, 李文胜. 扩展卡尔曼滤波在组合导航中的应用[J]. 传感器与微系统, 2017, 36(8): 158–160 doi: 10.13873/J.1000-9787(2017)08-0158-03

SHEN Kai, GUAN Xueyuan, and LI Wensheng. Application of EKF in integrated navigation system[J]. Transducer and Microsystem Technologies, 2017, 36(8): 158–160 doi: 10.13873/J.1000-9787(2017)08-0158-03

程兰, 王志远, 陈杰, 等. 基于粒子滤波和滑动平均扩展卡尔曼滤波的多径估计算法[J]. 电子与信息学报, 2017, 39(3): 709–716 doi: 10.11999/JEIT160587

CHENG Lan, WANG Zhiyuan, CHEN Jie, et al. An improved multipath estimation algorithm using particle filter and sliding average extended Kalman filter[J]. Journal of Electronics&Information Technology, 2017, 39(3): 709–716 doi: 10.11999/JEIT160587

朱海东, 葛万成. 车联网中的异构网络融合机制研究[J]. 通信技术, 2017, 50(8): 1691–1695 doi: 10.3969/j.issn.1002-0802.2017.08.017

ZHU Haidong and GE Wancheng. Heterogeneous network fusion mechanism in vehicle networks[J]. Communications Technology, 2017, 50(8): 1691–1695 doi: 10.3969/j.issn.1002-0802.2017.08.017

EVENSEN G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics[J]. Journal of Geophysical Research Oceans, 1994, 99(C5): 10143–10162 doi: 10.1029/94JC00572

VERLAAN M and HEEMINK A W. Nonlinearity in data assimilation applications: A practical method for analysis[J]. Monthly Weather Review, 2001, 129(6): 1578–1589 doi: 10.1175/1520-0493(2001)129

ZHANG Zequn, LI Heng, and ZHANG Dongxiao. Water flooding performance prediction by multi-layer capacitance-resistive models combined with the ensemble Kalman filter[J]. Journal of Petroleum Science&Engineering, 2015, 127(1): 1–19 doi: 10.1016/j.petrol.2015.01.020

ZHANG Zequn, LI Heng, and ZHANG Dongxiao. Reservoir characterization and production optimization using the ensemble-based optimization method and multi-layer capacitance-resistive models[J]. Journal of Petroleum Science&Engineering, 2017, 156: 633–653 doi: 10.1016/j.petrol.2017.06.020

LIU Di, MISHRA A K, and YU Zhongbo. Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering[J]. Journal of Hydrology, 2016, 538(1): 243–255 doi: 10.1016/j.jhydrol.2016.04.021

CUI Bo and ZHANG Jiashu. The improved ensemble Kalman filter for multisensor target tracking[C]. IEEE International Symposium on Information Science and Engineering, Shanghai, China, 2008: 263–265.

PORNSARAYOUTH S, WONGSAISUWAN M, and YAMAKITA M. An improvement of ensemble Kalman filter for OOSM tracking[J]. IFAC Proceedings Volumes, 2011, 44(1): 12003–12008 doi: 10.3182/20110828-6-it-1002.03399

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