| Citation: |
HUANG Jingying, MA Yuying, QIAO Zhiping, HUANG Chengzhang. A 3D Motion Estimation Method of Aerial Targets for Airborne IR Platforms[J]. Infrared Technology , 2025, 47(1): 97-107.
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The degrees of freedom in the motion state of aerial targets are higher, and the target motion state is more difficult to obtain. Existing methods focus on estimating the relative motion trajectory in two-dimensional space (azimuth and pitch), ignoring the interference of the reconnaissance platform's own attitude changes on the target's motion trajectory estimation, making direct application to airborne IR platform applications difficult. To address this problem, this study proposes a three-dimensional (3D) motion estimation method for airborne targets under an airborne IR platform to measure the target's motion status in all directions in the coordinate system of the northwest sky. To improve the accuracy of the target position estimation, this method introduces the target distance and the attitude of the detection platform to enhance the anti-interference performance of the IR target motion state estimation. Our method first uses a target-tracking module based on the TLD and a Kalman filter utilizing a detection-based tracking strategy. The Kalman filter is employed to alleviate the effects of target centroid jitter on the target position estimation accuracy. Second, a long-short strategic distance prediction module is proposed to supplement the target distance information not obtained by the laser rangefinder. Finally, the motion status of the target in each direction in the northwest-sky coordinate system is obtained using the aerial target motion estimation module based on prior information. Under the condition that the 3D motion information of the aerial target is known, the 2D spatial information of the target in the current reconnaissance system can be solved in reverse using this method. Experimental results show that the error in the target distance prediction result of this method is less than 50 m, and the velocity error of the northwest-sky coordinate system is less than 25 m/s. When the attitude angle of the detection system is changed, the target-tracking stability of this method is better than that of the Kalman filter.
| [1] |
黄楠楠, 刘贵喜, 张音哲, 等. 无人机视觉导航算法[J]. 红外与激光工程, 2016, 45(7): 726005-0726005(9).
HUANG Nannan, LIU Guixi, ZHANG Yinzhe, Unmanned aerial vehicle vision navigation algorithm[J]. Infrared and Laser Engineering, 2016, 45(7): 726005-0726005(9).
|
| [2] |
郑浩, 刘建芳, 廖梦怡. 室内视频监控下基于混合算法的人体异常行为检测和识别方法[J]. 计算机应用与软件, 2019, 36(7): 224-230. DOI: 10.3969/j.issn.1000-386x.2019.07.038
ZHENG Hao, LIU Jianfang, LIAO Mengyi. Human abnormal behavior detection and recognition based on hybrid algorithm in indoor video surveillance[J]. Computer Applications and Sofeware, 2019, 36(7): 224-230. DOI: 10.3969/j.issn.1000-386x.2019.07.038
|
| [3] |
王海涛, 王荣耀, 王文皞, 等. 目标跟踪综述[J]. 计算机测量与控制, 2020, 28(4) : 1-6.
WANG Haitao, WANG Rongyao, WANG Wenhao, et al. A review of target tracking[J]. Computerized Measurement and Control, 2020, 28(4): 1-6
|
| [4] |
刘雅婷, 王坤峰, 王飞跃. 基于踪片Tracklet关联的视觉目标跟踪: 现状与展望[J]. 自动化学报, 2017, 43(11): 1869-1885.
LIU Yating, WANG Kunfeng, WANG Feiyue. Tracklet association-based visual object tracking: the state of the art and beyond[J]. Acta Automatica Sinica, 2017, 43(11): 1869-1885.
|
| [5] |
程旭, 崔一平, 宋晨, 等. 基于时空注意力机制的目标跟踪算法[J]. 计算机科学, 2021, 48(4): 123-129.
CHENG Xu, CUI Yiping, SONG Chen, et al. Object tracking algorithm based on temporal-spatial attention mechanism[J]. Computer Science, 2021, 48(4): 123-129.
|
| [6] |
赵其杰, 屠大维, 高健, 等. 基于卡尔曼滤波的视觉预测目标跟踪及其应用[J]. 光学精密工程, 2008, 16(5): 937-942. DOI: 10.3321/j.issn:1004-924X.2008.05.029
ZHAO Qijie, TU Dawei, GAO Jian, et al. Kalman filter based vision predicting and object tracking method and its application[J]. Optics and Precision Engineering, 2008, 16(5): 937-942. DOI: 10.3321/j.issn:1004-924X.2008.05.029
|
| [7] |
Comaniciu D, Ramesh V. Mean shift and optimal prediction for efficient object tracking[C]//Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101). IEEE, 2000, 3: 70-73.
|
| [8] |
Henriques J F, Caseiro R, Martins P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//Computer Vision–ECCV 2012: 12th European Conference on Computer Vision, 2012: 702-715.
|
| [9] |
Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(3): 583-596.
|
| [10] |
Danelljan M, Hager G, Shahbaz Khan F, et al. Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 4310-4318.
|
| [11] |
WANG Naiyan, Dit-Yan Yeung. Learning a deep compact image representation for visual tracking[J]. Advances in Neural Information Processing Systems, 2013(1): 809-817.
|
| [12] |
WANG L, OUYANG W, WANG X, et al. Visual tracking with fully convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 3119-3127
|
| [13] |
Voigtlaender P, Luiten J, Torr P H S, et al. Siam r-cnn: Visual tracking by re-detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 6578-6588.
|
| [14] |
刘文, 胡琨林, 李岩, 等. 移动目标轨迹预测方法研究综述[J]. 智能科学与技术学报, 2021, 3(2): 149-160.
LIU Wen, HU Kunlin, LI Yan, et al. A review of prediction methods for moving target trajectories[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 149-160.
|
| [15] |
任条娟, 陈鹏, 陈友荣, 等. 基于深度学习的多目标运动轨迹预测算法[J]. 计算机应用研究, 2022, 39(1): 296-302.
REN Tiaojuan, CHEN Peng, CHEN Yourong, et al. Multi-target motion trajectory prediction algorithm based on deep learning[J]. Application Research of Computers, 2022, 39(1): 296-302.
|
| [16] |
熊光明, 鲁浩, 郭孔辉, 等. 基于滑动参数实时估计的履带车辆运行轨迹预测方法研究[J]. 兵工学报, 2017, 38(3): 600 -607.
XIONG Guangming, LU Hao, GUO Konghui, et al. Research on trajectory prediction of tracked vehicles based on real time slip estimation[J]. Acta Armamentarii, 2017, 38(3): 600-607.
|
| [17] |
杨福威, 孟红, 朱强. 基于模型预测控制的履带式无人平台轨迹跟踪控制算法研究[J]. 船舰电子工程, 2018, 38(3): 44-50.
YANG Fuwei, MENG Hong, ZHU Qiang. Research on tracking control algorithm of tracked unmanned platform based on model predictive control[J]. Ship Electronic Engineering, 2018, 38(3): 44-50.
|
| [18] |
MIN K, Kim D, Park J, et al. RNN-based path prediction of obstacle vehicles with deep ensemble[J]. IEEE Transactions on Vehicular Technology, 2019, 68(10): 10252-10256.
|
| [19] |
Altché F, de La Fortelle A. An LSTM network for highway trajectory prediction[C]//2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017: 353-359.
|
| [20] |
Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 34(7): 1409-1422.
|
| [21] |
杨德振, 黄静颖, 喻松林, 等. 基于引导滤波和分块自适应阈值的单帧红外弱小目标检测[J]. 光子学报, 2023, 52(4): 0410005.
YANG Dezhen, HUANG Jingying, YU Songlin, et al. Single-frame infrared dim target detection based on guided filter and segmented adaptive thresholds[J]. Acta Photonica Sinica, 2023, 52(4): 0410005.
|
| [22] |
Bodla N, Singh B, Chellappa R, et al. Soft-NMS-improving object detection with one line of code[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017: 5562-5570, Doi: 10.1109/ICCV.2017.593.
|
| [23] |
LI W, ZHAO M, DENG X, et al. Infrared small target detection using local and nonlocal spatial information[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(9): 3677-3689.
|
| [24] |
陆剑锋, 林海, 潘志庚. 自适应区域生长算法在医学图像分割中的应用[J]. 计算机辅助设计与图形学学报, 2005, 17(10): 2168-2173.
LU Jianfeng, LIN Hai, PAN Zhigeng. Adaptive region growing algorithm in medical images segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2005, 17(10): 2168-2173.
|
| [25] |
余旺盛, 侯志强, 宋建军. 基于标记分水岭和区域合并的彩色图像分割[J]. 电子学报, 2011, 39(5): 1007-1012.
YU Wangsheng, HOU Zhiqiang, SONG Jianjun. Color image segmentation based on marked-watershed and region-merge[J]. Acta Electronica Sinica, 2011, 39(5): 1007-1012.
|
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