Spatiotemporal Registration for Multi-sensor Fusion Systems

被引:0
|
作者
Bu, Shi-zhe [1 ]
Zhou, Gong-jian [1 ]
机构
[1] Harbin Inst Technol, Res Inst Elect Engn, Harbin 150001, Peoples R China
来源
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTER SCIENCE (AICS 2016) | 2016年
基金
中国国家自然科学基金;
关键词
Spatiotemporal registration; Data fusion; Unscented Kalman filter (UKF); State augmentation method;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A method of online sensor fusion with spatially and temporally misaligned sensors is considered in this paper. A spatiotemporal registration mode is developed for sensor alignment, and augment the target state vector with spatiotemporal bias. An unscented Kalman filter (UKF) is used to fuse and register these sensors, then the target state estimation as well as the spatial and temporal bias estimation can be obtained simultaneously. Simulations show that the proposed algorithm not only can align these sensors properly with both spatial and temporal bias, but can also obtain accurate fused tracks.
引用
收藏
页码:333 / 339
页数:7
相关论文
共 50 条
  • [41] An estimator for multi-sensor data fusion
    Thejaswi, C.
    Ganapathy, V.
    Patro, R. K.
    Raina, M.
    Ghosh, S. K.
    2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS, 2006, : 2690 - +
  • [42] Analysis of Multi-sensor Image Fusion
    Xu, Yan
    2018 5TH INTERNATIONAL CONFERENCE ON ELECTRICAL & ELECTRONICS ENGINEERING AND COMPUTER SCIENCE (ICEEECS 2018), 2018, : 338 - 341
  • [43] Qualitative multi-sensor data fusion
    Falomir, Z
    Escrig, AT
    RECENT ADVANCES IN ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2004, 113 : 259 - 266
  • [44] Multi-sensor data fusion architecture
    Al-Dhaher, AHG
    Mackesy, D
    3RD IEEE INTERNATIONAL WORKSHOP ON HAPTIC, AUDIO AND VISUAL ENVIRONMENTS AND THEIR APPLICATIONS - HAVE 2004, 2004, : 159 - 163
  • [45] Study of multi-sensor fusion for localization
    Pelka, Michal
    Majek, Karol
    Ratajczak, Jakub
    Bedkowski, Janusz
    Maslowski, Andrzej
    2019 IEEE INTERNATIONAL SYMPOSIUM ON SAFETY, SECURITY, AND RESCUE ROBOTICS (SSRR), 2019, : 110 - 111
  • [46] Research on fusion algorithm for multi-sensor target tracking in nonlinear systems
    Yang, Chun-Ling
    Liu, Guo-Sui
    Yu, Ying-Lin
    Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2000, 21 (06): : 512 - 515
  • [47] Fusion estimation for multi-sensor networked systems with packet loss compensation
    Ding, Jian
    Sun, Shuli
    Ma, Jing
    Li, Na
    INFORMATION FUSION, 2019, 45 : 138 - 149
  • [48] Distributed Fusion Filter for Nonlinear Multi-Sensor Systems With Correlated Noises
    Hao, Gang
    Sun, Shuli
    IEEE ACCESS, 2020, 8 : 39548 - 39560
  • [49] Principles of data-fusion in multi-sensor systems for nondestructive testing
    Chioclea, S
    Dickstein, P
    REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION, VOLS 19A AND 19B, 2000, 509 : 805 - 807
  • [50] Multiscale data fusion for multi-sensor single model dynamic systems
    Wen, C.L.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2001, 29 (03): : 341 - 345