Volume 45 Issue 5
Jun.  2016
Turn off MathJax
Article Contents
Article Navigation >  Infrared and Laser Engineering  > 2016  >  45(5): 526001-0526001(6)

Tian Yuexin, Gao Kun, Liu Ying, Lu Yan, Ni Guoqiang. A novel multi-band infrared mutation point target detection method based on generalized cumulative sum[J]. Infrared and Laser Engineering, 2016, 45(5): 526001-0526001(6). doi: 10.3788/IRLA201645.0526001
Citation: Tian Yuexin, Gao Kun, Liu Ying, Lu Yan, Ni Guoqiang. A novel multi-band infrared mutation point target detection method based on generalized cumulative sum[J]. Infrared and Laser Engineering, 2016, 45(5): 526001-0526001(6). doi: 10.3788/IRLA201645.0526001
shu

A novel multi-band infrared mutation point target detection method based on generalized cumulative sum

doi: 10.3788/IRLA201645.0526001
  • 1.

    Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education of China,Beijing Institute of Technology,Beijing 100081,China;

  • 2.

    North China Institute of Optoelectronic Technology,Beijing 100015,China

  • Received Date: 2015-09-10
  • Rev Recd Date: 2015-10-11
  • Publish Date: 2016-05-25
  • The detection for mutation point which suddenly appear and vanish has received much attention in IRST application. With the development of multi-wave band IRST system, a sequential infrared target trajectory detection algorithm based on generalized cumulative sum was proposed. The research extended the single band detection into multiple band detection, which improved the detection probability, greatly reduced the average detection delay and signal-to-noise ratio(SNR) under a certain false alarm rate. Results of simulation analysis show that the modified algorithm has excellent detection performance for infrared mutation point target. In case of a certain false alarm rate, the SNR threshold can be decreased to 60% and dual band detection delay can be reduced about a half comparing with the traditional detection method.
  • [1] Gong Junliang, He Xin, Wei Zhonghui, et al. Infrared dim and small target detection method using scale-space theory[J]. Infrared and Laser Engineering, 2013, 42(9): 2566-2573. (in Chinese)龚俊亮, 何昕, 魏仲慧, 等. 采用尺度空间理论的红外弱小目标检测方法[J]. 红外与激光工程, 2013, 42(9): 2566-2573.
    [2] Qu Shiru, Yang Honghong. Multi-target detection and tracking of video sequence based on Kalman_BP neural network[J]. Infrared and Laser Engineering, 2013, 42(9): 2553-2560. (in Chinese)曲仕茹, 杨红红. 采用Kalman_BP神经网络的视频序列多目标检测与跟踪[J]. 红外与激光工程, 2013, 42(9): 2553-2560.
    [3] Li Yimang, Wei Zhonghui, Guo Jingming. Infrared targets detection and recognition using dimension reduction technology[J]. Optics and Precision Engineering, 2013, 21(5): 1297-1303. (in Chinese)李一芒, 魏仲慧, 郭敬明. 采用降维技术的红外目标检测与识别[J]. 光学精密工程, 2013, 21(5): 1297-1303.
    [4] Wang Gang, Yu Bingxi. Approach to estimate infrared point-target detection range against sky background based on contrast[J]. Optics and Precision Engineering, 2002, 10(3): 276-280. (in Chinese)王刚, 禹秉熙. 基于对比度的空中红外点目标探测距离估计方法[J]. 光学精密工程, 2002, 10(3): 276-280.
    [5] Lv Yinhuan, Zhang Tao, Tong Guanghui. Design of optical system based on diffraction optics for infrared weak target detection[J]. Chinese Optics, 2009, 2(6): 543-549. (in Chinese)吕银环, 张涛, 童广辉. 基于衍射光学元件的红外弱目标探测系统设计[J]. 中国光学, 2009, 2(6): 543-549.
    [6] Li Bo. Application and development trend of infrared stealth technology[J]. Chinese Optics, 2013, 6(6): 818-823. (in Chinese)李波. 红外隐身技术的应用及发展趋势[J]. 中国光学, 2013, 6(6): 818-823.
    [7] Reed I S, Gagliardi R M, Shao H M. Application of three-dimensional filtering to moving target detection[J]. IEEE Trans AES, 1983, 19(6): 898-904.
    [8] Barniv Y, Kella Offer. Dynamic programming solution for detecting dim moving targets[J]. IEEE Trans AES, 1987, 21(11): 776-788.
    [9] Rollason M, Samond D J. A particle filter for track-before-detect of a target with unknown amplitude[C]//IEE Seminar on Target Tracking: Algorithms and Applications, 2001.
    [10] Wang Luping, Zhang Luping, Han Jiantao. Detecting algorithm of moving target in dynamic background based on gray-weighted kernel function[J]. Infrared and Laser Engineering, 2013, 42(12): 3453-3457. (in Chinese)王鲁平, 张路平, 韩建涛. 采用灰度加权核函数的动态背景运动目标检测算法[J]. 红外与激光工程, 2013, 42(12): 3453-3457.
    [11] Wald A. Sequential Analysis[M]. New York: Wiley, 1947: 50-55.
    [12] McConnell I, Oliver C J. A comparison of segmentation methods with standard CFAR for point target detection[C]//SPIE, 1998, 3497: 76-87.
    [13] Page E S. A test for a change in a parameter occurring at an unknown point[J]. Biometrika, 1955, 42(3/4): 523-527.
    [14] Roberts S W. Control chart tests based on geometric moving average[J]. Technometrics, 2000, 42(1): 97-101.
    [15] Emanuele Grossi, Marco Lops. Sequential detection of Markov targets with trajectory estimation[J]. IEEE Trans Inf Theory, 2008, 54(9): 4144-4154.
    [16] Chen Li, Han Chongzhao, Zhu Hongyan. Detection performance loss due to jitter in naval IRST systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(1): 326-338.
    [17] Tartakovsky A, Blazek R. Effective adaptive spatial-temporal technique for clutter rejection in IRST[C]//SPIE Proceedings: Signal and Data Processing of Small Targets, 2000, 4048: 85/1-11.
    [18] Tartakovsky A, Kligys S, Petrov A. Adaptive sequential algorithms for detecting targets in a heavy IR clutter[C]//SPIE Proceedings: Signal and Data Processing of Small Targets, 1999, 3809: 119/1-12.
    [19] Alexander G Tartakovsky, Venugopal V Veeravalli. An efficient sequential procedure for detecting changes in multichannel and distributed systems[C]//ISIF, 2002, 1: 41-48.
    [20] Alexander G Tartakovsky, Li X R, George Yaralov. Sequential detection of targets in multichannel systems[J]. IEEE Trans Inf Theory, 2003, 49(2): 425-445.
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Get Citation
PDF
XML

Article Metrics

Article views(406) PDF downloads(156) Cited by()

Related
Proportional views

A novel multi-band infrared mutation point target detection method based on generalized cumulative sum

doi: 10.3788/IRLA201645.0526001
  • 1. Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education of China,Beijing Institute of Technology,Beijing 100081,China;
  • 2. North China Institute of Optoelectronic Technology,Beijing 100015,China
  • Received Date: 2015-09-10
  • Rev Recd Date: 2015-10-11
  • Publish Date: 2016-05-25

Abstract: The detection for mutation point which suddenly appear and vanish has received much attention in IRST application. With the development of multi-wave band IRST system, a sequential infrared target trajectory detection algorithm based on generalized cumulative sum was proposed. The research extended the single band detection into multiple band detection, which improved the detection probability, greatly reduced the average detection delay and signal-to-noise ratio(SNR) under a certain false alarm rate. Results of simulation analysis show that the modified algorithm has excellent detection performance for infrared mutation point target. In case of a certain false alarm rate, the SNR threshold can be decreased to 60% and dual band detection delay can be reduced about a half comparing with the traditional detection method.

    HTML

Reference (20)

Catalog

    版权所有 © 2019 《红外与激光工程》编辑部｜ Address: 58 Zhonghuan West Road, Tianjin Airport Economic Zone｜ Post Code: 300308

    津ICP备19007885号-1

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return