The performance analysis of three-dimensional track-before-detect algorithm based on Fisher-Tippett-Gnedenko theorem

Abstract The dim moving target tracking from the infrared image sequence in the presence of high clutter and noise has been recently under intensive investigation. The track-before-detect (TBD) algorithm processing the image sequence over a number of frames before decisions on the target track and existence is known to be especially attractive in very low SNR environments ( ⩽ 3 dB). In this paper, we shortly present a three-dimensional (3-D) TBD with dynamic programming (TBD-DP) algorithm using multiple IR image sensors. Since traditional two-dimensional TBD algorithm cannot track and detect the along the viewing direction, we use 3-D TBD with multiple sensors and also strictly analyze the detection performance (false alarm and detection probabilities) based on Fisher-Tippett-Gnedenko theorem. The 3-D TBD-DP algorithm which does not require a separate image registration step uses the pixel intensity values jointly read off from multiple image frames to compute the merit function required in the DP process. Therefore, we also establish the relationship between the pixel coordinates of image frame and the reference coordinates.

[1]  Zhengzhou Li,et al.  Infrared small moving target detection algorithm based on joint spatio-temporal sparse recovery , 2015 .

[2]  Wei Yi,et al.  Track-before-detect strategies for range distributed target detection in compound-Gaussian clutter , 2016, Signal Process..

[3]  PAUL EMBRECHTS,et al.  Modelling of extremal events in insurance and finance , 1994, Math. Methods Oper. Res..

[4]  Joohwan Chun,et al.  A new TBD-DP algorithm using multiple IR sensors to locate the target launch point , 2011, Security + Defence.

[5]  Thia Kirubarajan,et al.  PHD filter based track-before-detect for MIMO radars , 2012, Signal Process..

[6]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[7]  Xiaoming Huo,et al.  Object tracking under low signal-to-noise-ratio with the instantaneous-possible-moving-position model , 2013, Signal Process..

[8]  Yair Barniv,et al.  Dynamic Programming Solution for Detecting Dim Moving Targets , 1985, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Zhengzhou Li,et al.  Dim moving target detection algorithm based on spatio-temporal classification sparse representation , 2014 .

[10]  Jianyu Yang,et al.  A Modified Dynamic Programming Approach for Dim Target Detection and Tracking , 2009, 2009 2nd International Congress on Image and Signal Processing.

[11]  Larry B. Stotts,et al.  Optical moving target detection with 3-D matched filtering , 1988 .

[12]  Offer Kella,et al.  Dynamic Programming Solution for Detecting Dim Moving Targets Part II: Analysis , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[13]  S. W. Shaw,et al.  Efficient target tracking using dynamic programming , 1993 .

[14]  Vikram Krishnamurthy,et al.  Performance analysis of a dynamic programming track before detect algorithm , 2002 .

[15]  Robin J. Evans,et al.  Peformance of dynamic programming techniques for Track-Before-Detect , 1996 .

[16]  I. Reed,et al.  A recursive moving-target-indication algorithm for optical image sequences , 1990 .

[17]  Donald Reid.  An algorithm for tracking multiple targets , 1978 .