Infrared and Laser Engineering, Volume. 51, Issue 7, 20210614(2022)

Anti-interference recognition method of aerial infrared targets based on a spatio-temporal correlation inference network

Liang Zhang1,2, Xiaoqian Tian3, Shaoyi Li3、*, and Xi Yang3
Author Affiliations
  • 1China Airborne Missile Academy, Luoyang 471009, China
  • 2Aviation Key Laboratory of Science and Technology on Airborne Guided Weapon, Luoyang 471009, China
  • 3School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
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    Figures & Tables(10)
    Representation diagram of space-time correlation inference network
    Framework diagram of anti-interference recognition algorithm based on space-time correlation inference network
    Schematic diagram of target and interference sample labeling
    Initial network structure diagram
    Network structure diagram based on space-time correlation inference
    Test chart of target recognition algorithm based on space-time correlation inference network
    • Table 1. Interference projection strategy

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      Table 1. Interference projection strategy

      Total decoysProjection group numberGroup interval/sNumber of decoys per groupDecoys interval/sManeuver
      2424110.1Without maneuver, turn left, jump
      2412120.1Without maneuver, turn left, jump
      246140.1Without maneuver, turn left, jump
    • Table 2. Time slice conditional probability table of area feature node

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      Table 2. Time slice conditional probability table of area feature node

      C=1
      Y1 Y2 Y3 Y25
      Z1 0000
      Z2 0.220 00.002 500
      Z3 0.778 80.849 70.156 30
      Z4 0.001 20.147 80.673 70
      Z5 06.749 3e-050.168 20
      Z29 0000
      C=0
      Y1 Y2 Y3 Y25
      Z1 0.475 90.039 000
      Z2 0.392 00.479 50.047 30
      Z3 0.129 30.328 30.401 90
      Z4 0.002 90.105 20.384 10
      Z5 00.048 00.070 30
      Z29 0000
    • Table 3. Transition probability table of area feature node

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      Table 3. Transition probability table of area feature node

      C=1
      注:片内概率表面积的父节点是周长;片间转移概率表面积的父节点是当前时刻的周长以及上一时刻的面积。面积特征节点的转移概率表是三维概率表,表3选取了当前时刻面积的第五个特征区间对应的父节点的转移概率表。
      Y1 Y2 Y3 Y25
      Z1 0000
      Z2 000.162 40
      Z3 05.419 84-060.008 40
      Z4 03.210 0e-050.041 30
      Z5 00.975 60.945 90
      Z29 0000
      C=0
      Y1 Y2 Y3 Y25
      Z1 00.006 70.007 30
      Z2 00.004 30.042 80
      Z3 04.022 5e-040.056 10
      Z4 04.161 5e-040.027 10
      Z5 00.027 30.051 80
      Z29 0000
    • Table 4. Airborne infrared target recognition algorithm test results of two algorithms

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      Table 4. Airborne infrared target recognition algorithm test results of two algorithms

      Launch distance/mRelative azimuthType of maneuverLaunch conditionsTAN algorithm accuracyAlgorithm accuracy of this paper
      Firing intervalNumber of decoys launchedNumber of launch groups
      700010°Turn left0.422490.7393.29
      700010°Jump0.422487.2294.79
      700010°Turn left0.441283.0692.91
      700010°Jump0.441288.1893.93
      700010°Turn left0.722493.9592.54
      700010°Jump0.722490.5494.41
      700010°Turn left0.741286.7294.72
      700010°Jump0.741291.5494.83
      700040°Turn left0.422489.2293.13
      700040°Jump0.422493.2394.22
      700040°Turn left0.441285.2294.56
      700040°Jump0.441293.4895.05
      700040°Turn left0.722491.9293.87
      700040°Jump0.722495.8896.87
      700040°Turn left0.741286.8291.07
      700040°Jump0.741296.1395.16
      7000100°Turn left0.422485.5192.32
      7000100°Jump0.422495.7795.93
      7000100°Turn left0.441294.6595.33
      7000100°Jump0.441295.3296.55
      7000100°Turn left0.722485.9292.54
      7000100°Jump0.722498.1494.60
      7000100°Turn left0.741297.0796.63
      7000100°Jump0.741297.7594.81
      7000160°Turn left0.422492.5095.15
      7000160°Jump0.422491.2591.91
      7000160°Turn left0.441294.7595.06
      7000160°Jump0.441292.0093.72
      7000160°Turn left0.722495.0496.01
      7000160°Jump0.722493.7594.14
      7000160°Turn left0.741297.3397.42
      7000160°Jump0.741294.5795.57
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    Liang Zhang, Xiaoqian Tian, Shaoyi Li, Xi Yang. Anti-interference recognition method of aerial infrared targets based on a spatio-temporal correlation inference network[J]. Infrared and Laser Engineering, 2022, 51(7): 20210614

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    Paper Information

    Category: Image processing

    Received: Aug. 27, 2021

    Accepted: --

    Published Online: Dec. 20, 2022

    The Author Email: Shaoyi Li (amlishaoyi2008@163.com)

    DOI:10.3788/IRLA20210614

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