Výsledky vyhledávání - IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications~

  1. 1

    Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion Autor Li, Zhengze, Xu, Jiancheng

    ISSN: 1687-5265, 1687-5273, 1687-5273
    Vydáno: United States Hindawi 2021
    “…) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking…”
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    Journal Article
  2. 2

    An algorithm for multi-sensor data fusion target tracking Autor Liu Guo-cheng, Wang Yong-ji

    ISBN: 9781424417339, 1424417333
    ISSN: 1948-9439
    Vydáno: IEEE 01.07.2008
    “…The principle of target tracking and data fusion techniques are discussed. To resolve enormous uncertainty that exists in sensors of mobile robots, the cross-sensor and cross-modality(CSCM…”
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    Konferenční příspěvek
  3. 3

    Spatial Target Tracking Algorithm Based on Heterogeneous Sensors Data Fusion Autor Xu, Yuan, Dong, Jian, Bai, Jinliang, Zhang, Xinyu, Zhang, Ran

    Vydáno: IEEE 14.05.2023
    “… The simulation example is given. The results show that this is an effective method for heterogeneous data fusion to track space targets…”
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  4. 4

    Distributed data fusion algorithms for tracking a maneuvering target Autor Li-Wei Fong

    Vydáno: IEEE 01.07.2007
    “…) and information matrix fusion (IMF), in a multi-sensor environment for computing the fused estimates from distributed Kahnan filters tracking a single maneuvering target…”
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  5. 5

    Stern target tracking algorithm based on data fusion Autor Qiu, Boxiang, Wang, Xinwei, Gan, Xingli, Li, Xiangning

    Vydáno: IEEE 27.06.2025
    “…This paper proposes a stern target detection and tracking algorithm based on multi-sensor decision-level fusion…”
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  6. 6

    Application of Data Sensor Fusion Using Extended Kalman Filter Algorithm for Identification and Tracking of Moving Targets from LiDAR–Radar Data Autor Montañez, Oscar Javier, Suarez, Marco Javier, Fernandez, Eduardo Avendano

    ISSN: 2072-4292, 2072-4292
    Vydáno: Basel MDPI AG 01.07.2023
    Vydáno v Remote sensing (Basel, Switzerland) (01.07.2023)
    “… The resolution in the acquisition of data can be improved by integrating data sensor fusion systems to measure the same physical phenomenon from two or more sensors…”
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    Journal Article
  7. 7

    Cluster-based centralized data fusion for tracking maneuvering targets using interacting multiple model algorithm Autor Vaidehi, V., Kalavidya, K., Gandhi, S. Indira

    ISSN: 0256-2499, 0973-7677
    Vydáno: 01.04.2004
    Vydáno v Sadhana (Bangalore) (01.04.2004)
    “… Tracking accuracy can be further improved using data fusion. Tracking of multiple targets using multiple sensors and fusing them at a central site using centralized…”
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    Journal Article
  8. 8

    Research on Data Fusion Algorithm Based on Deep Learning in Target Tracking Autor Wu, Huihui

    ISSN: 2331-8422
    Vydáno: Ithaca Cornell University Library, arXiv.org 23.11.2022
    Vydáno v arXiv.org (23.11.2022)
    “…) into a deep long and short-term memory network, developed a new network structure and designed a fusion strategy, an eye tracking data fusion algorithm based on long and short-term memory network is proposed…”
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    Paper
  9. 9

    Measurement Data Fusion Based on Optimized Weighted Least-Squares Algorithm for Multi-Target Tracking Autor He, Xinxin, Wang, Tao, Liu, Wei, Luo, Tao

    ISSN: 2169-3536, 2169-3536
    Vydáno: Piscataway IEEE 2019
    Vydáno v IEEE access (2019)
    “… on range-based multi-target tracking in wireless sensor networks. In this paper, we develop an improved weighted least-square algorithm based on an enhanced non-naive Bayesian classifier (ENNBC) method…”
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    Journal Article
  10. 10

    Retracted: Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion Autor Intelligence and Neuroscience, Computational

    ISSN: 1687-5265, 1687-5273, 1687-5273
    Vydáno: United States Hindawi 2023
    “…[This retracts the article DOI: 10.1155/2021/4276860.]…”
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    Journal Article
  11. 11

    An algorithm based on hierarchical clustering for multi-target tracking of multi-sensor data fusion Autor Wang, Hao, Liu, Tangxing, Bu, Qing, Yang, Bo

    ISSN: 1934-1768
    Vydáno: TCCT 01.07.2016
    Vydáno v Chinese Control Conference (01.07.2016)
    “…This paper proposes an efficient algorithm to deal with multi-target tracking of multi-sensor data fusion…”
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  12. 12

    Multi sensor data fusion algorithms for target tracking using multiple measurements Autor Anitha, R., Renuka, S., Abudhahir, A.

    ISBN: 1479915947, 9781479915941
    Vydáno: IEEE 01.12.2013
    “… The objective of this work is to evaluate multi sensor data fusion algorithms for target tracking…”
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  13. 13

    Data fusion algorithms for sea target tracking using coastal radar model Autor Vervecka, Martynas

    ISBN: 1424456134, 9781424456130
    ISSN: 2155-5745
    Vydáno: IEEE 01.06.2010
    Vydáno v 11-th INTERNATIONAL RADAR SYMPOSIUM (01.06.2010)
    “… Sea target tracking algorithm is modeled, coast radar system model implemented, multiple ships tracking and detection is tested…”
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  14. 14

    Fast Data Fusion algorithm for tracking maneuvering target by vehicle formation Autor Runle Du, Jiaqi Liu, Yonghai Wang, Zhifeng Li, Chunyue Gao

    ISBN: 9781467357920, 1467357928
    ISSN: 1097-5659
    Vydáno: IEEE 01.04.2013
    “…When multiple flying vehicles with bearing-only tracking collaborate in locating a target, observation data fusion is required to compensate the drift of navigation system, the noise from observation…”
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  15. 15

    Multiple arrays multiple targets data fusion and tracking based on TDOA measurement using FCM-JPDA algorithm Autor JinYu He, YiJian Yan, Qiao Meng

    ISBN: 9780780374881, 0780374886
    Vydáno: IEEE 2002
    “…) method for multi-target tracking based on TDOA (time difference of arrival) measurements. This method is used in a tracking system with an array group which has several subarrays…”
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  16. 16

    Target Ship Recognition and Tracking with Data Fusion Based on Bi-YOLO and OC-SORT Algorithms for Enhancing Ship Navigation Assistance Autor Chen, Shuai, Gao, Miao, Shi, Peiru, Zeng, Xi, Zhang, Anmin

    ISSN: 2077-1312, 2077-1312
    Vydáno: Basel MDPI AG 01.02.2025
    “… Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information…”
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    Journal Article
  17. 17

    Multi-platform multi-target tracking fusion via covariance intersection: using fuzzy optimised modified Kalman filters with measurement noise covariance estimation Autor Wren, T.J, Mahmood, A

    ISBN: 0863419100, 9780863419102
    Vydáno: Stevenage IET 2008
    “…Presented in this paper is a detailed novel approach to tracking multiple moving targets from multiple moving platforms and fusing the individual estimates within platform centric nodes via covariance intersection…”
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  18. 18

    Fusion of novel biometrics for human tracking and identification Autor Nothard, J, Hargreaves, J, Goffredo, M, Nixon, P.M, Carter, J, Pearce, D, Harvey, C

    ISBN: 0863419100, 9780863419102
    Vydáno: Stevenage IET 2008
    “…QinetiQ and Southampton University are engaged in a programme on fusion of novel biometrics for real-world secure environments…”
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  19. 19

    Multi-sensor debris tracking Autor Welford, J

    ISBN: 0863419100, 9780863419102
    Vydáno: Stevenage IET 2008
    “…We discuss the problem of tracking numerous debris objects originating from a point source, such as fragments produced by a collision or explosion…”
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  20. 20

    Tracking multiple targets from a mobile robot platform using a laser range scanner Autor Kondaxakis, P, Kasderidis, S, Trahanias, P

    ISBN: 0863419100, 9780863419102
    Vydáno: Stevenage IET 2008
    “… The algorithm utilizes real laser-scanner data to dynamically extract moving objects from their background environment, using a time-fading grid map method, and tracks the identified targets…”
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