改进粒子滤波的弱小目标跟踪
Dim small target tracking based on improved particle filter
查看参考文献21篇
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文摘
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针对低信噪比(SNR<3 dB)场景下弱小目标跟踪问题,提出了改进的粒子滤波跟踪方法。本文首先通过空间位置加权的方式来获取灰度特征,并将邻域运动模型和灰度概率图相结合来获取弱小目标运动特征,然后构建灰度与运动特性的联合观测模型来计算粒子权值。同时在跟踪过程中考虑到目标的灰度分布特性并不稳定,加入了自适应更新参考目标灰度模板的策略,最后采用几组真实场景来验证本文算法的跟踪效果。实验证明:和传统算法相比,本文算法增强了低信噪比(SNR<3 dB)场景下红外弱小目标跟踪能力。 |
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其他语种文摘
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As to solve the problem of dim small target tracking in low signal-to-noise ratio (SNR<3 dB) scenes, an improved particle filter tracking method is proposed. This paper firstly obtains the gray feature by spatial position weighting method, and combines the neighborhood motion model and the gray probability graph to get the motion features of dim small target. Then construct the joint observation model of gray and motion features to calculate the particle weights. At the same time, in the process of tracking, the gray distribution of the target is not stable, and the strategy of adaptively updating the gray template of reference target is added. Finally, the sequence image is used to prove the tracking effect of dim small target. Experiments show that compared with the traditional particle filter algorithm, the proposed method greatly enhanced the tracking ability of dim small target in low SNR (SNR<3 dB) scenes. |
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来源
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光电工程
,2018,45(8):170569-1-170569-10 【核心库】
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DOI
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10.12086/oee.2018.170569
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关键词
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弱小目标
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跟踪
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粒子滤波
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特征融合
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地址
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1.
中国科学院光电技术研究所, 四川, 成都, 610209
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电子科技大学光电科学与工程学院, 四川, 成都, 610054
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中国科学院大学, 北京, 100049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1003-501X |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金资助项目
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文献收藏号
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CSCD:6304505
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参考文献 共
21
共2页
|
|
1.
苗晓孔. 改进Sobel算子的单帧红外弱小目标检测.
光电工程,2016,43(12):119-125
|
被引
5
次
|
|
|
|
|
2.
王鑫. 一种新的复杂背景下红外弱小目标检测方法.
系统仿真学报,2009,21(20):6568-6572
|
被引
3
次
|
|
|
|
|
3.
Wang X. Infrared human tracking with improved mean shift algorithm based on multicue fusion.
Applied Optics,2009,48(21):4201-4212
|
被引
4
次
|
|
|
|
|
4.
王继平. 基于Kalman滤波的红外弱小目标检测前跟踪算法.
装备学院学报,2012,23(2):72-77
|
被引
2
次
|
|
|
|
|
5.
Zhan R H. Iterated unscented Kalman filter for passive target tracking.
IEEE Transactions on Aerospace and Electronic Systems,2007,43(3):1155-1163
|
被引
37
次
|
|
|
|
|
6.
康莉. 基于unscented粒子滤波的红外弱小目标跟踪.
系统工程与电子技术,2007,29(1):1-4
|
被引
9
次
|
|
|
|
|
7.
Han H. Particle filter for state estimation of jump Markov nonlinear system with application to multi-targets tracking.
International Journal of Systems Science,2013,44(7):1333-1343
|
被引
2
次
|
|
|
|
|
8.
Han H. An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking.
Computers & Mathematics with Applications,2011,62(7):2685-2695
|
被引
8
次
|
|
|
|
|
9.
种衍文. 一种多特征自适应融合的粒子滤波红外目标跟踪方法.
武汉大学学报·信息科学版,2016,41(5):598-604
|
被引
4
次
|
|
|
|
|
10.
王鑫. 复杂背景下基于改进粒子滤波的红外人体跟踪.
系统仿真学报,2010,22(10):656-663
|
被引
1
次
|
|
|
|
|
11.
王鑫. 基于特征融合的粒子滤波在红外小目标跟踪中的应用.
中国图象图形学报,2010,15(1):91-97
|
被引
11
次
|
|
|
|
|
12.
Wang W G. A robust infrared dim target detection method based on template filtering and saliency extraction.
Infrared Physics & Technology,2015,73:19-28
|
被引
6
次
|
|
|
|
|
13.
Wang X. A sparse representation-based method for infrared dim target detection under sea-sky background.
Infrared Physics & Technology,2015,71:347-355
|
被引
6
次
|
|
|
|
|
14.
Li Z Z. Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online.
Sensors,2014,14(6):9451-9470
|
被引
9
次
|
|
|
|
|
15.
Wang L J. Visual tracking with fully convolutional networks.
Proceedings of 2015 IEEE International Conference on Computer Vision,2015
|
被引
1
次
|
|
|
|
|
16.
Ma C. Hierarchical convolutional features for visual tracking.
Proceedings of 2015 IEEE International Conference on Computer Vision,2015
|
被引
2
次
|
|
|
|
|
17.
Yang X. Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks.
Remote Sensing,2018,10(1):132-139
|
被引
32
次
|
|
|
|
|
18.
王鑫.
复杂背景下红外目标检测与跟踪算法研究,2010
|
被引
8
次
|
|
|
|
|
19.
Fan X. Dim small targets detection based on self-adaptive caliber temporal-spatial filtering.
Infrared Physics & Technology,2017,85:465-477
|
被引
2
次
|
|
|
|
|
20.
张强. 基于局部极大值的红外弱小目标分割方法.
红外技术,2011,33(1):41-44
|
被引
9
次
|
|
|
|
|
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