Small tracking error correction for moving targets of intelligent electro-optical detection systems

  • Cheng SHEN ,
  • Zhijie WEN ,
  • Wenliang ZHU ,
  • Dapeng FAN ,
  • Mingyuan LING
Expand
  • College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
wenzhijie@nudt.edu.cn

Received date: 10 Sep 2023

Accepted date: 07 Jan 2024

Copyright

2024 Higher Education Press

Abstract

Small tracking error correction for electro-optical systems is essential to improve the tracking precision of future mechanical and defense technology. Aerial threats, such as “low, slow, and small (LSS)” moving targets, pose increasing challenges to society. The core goal of this work is to address the issues, such as small tracking error correction and aiming control, of electro-optical detection systems by using mechatronics drive modeling, composite velocity–image stability control, and improved interpolation filter design. A tracking controller delay prediction method for moving targets is proposed based on the Euler transformation model of a two-axis, two-gimbal cantilever beam coaxial configuration. Small tracking error formation is analyzed in detail to reveal the scientific mechanism of composite control between the tracking controller’s feedback and the motor’s velocity–stability loop. An improved segmental interpolation filtering algorithm is established by combining line of sight (LOS) position correction and multivariable typical tracking fault diagnosis. Then, a platform with 2 degrees of freedom is used to test the system. An LSS moving target shooting object with a tracking distance of S = 100 m, target board area of A = 1 m2, and target linear velocity of v = 5 m/s is simulated. Results show that the optimal method’s distribution probability of the tracking error in a circle with a radius of 1 mrad is 66.7%, and that of the traditional method is 41.6%. Compared with the LOS shooting accuracy of the traditional method, the LOS shooting accuracy of the optimized method is improved by 37.6%.

Cite this article

Cheng SHEN , Zhijie WEN , Wenliang ZHU , Dapeng FAN , Mingyuan LING . Small tracking error correction for moving targets of intelligent electro-optical detection systems[J]. Frontiers of Mechanical Engineering, 2024 , 19(2) : 11 . DOI: 10.1007/s11465-024-0782-6

Nomenclature

Abbreviations
BLDC Brushless direct-current motor
DOF Degree of freedom
EODS Electro-optical detection system
FOV Field of view
LOS Line of sight
LSS Low, slow, and small
MD Miss distance
PID Proportional–integral–differential
Variables
A Target board area
B Viscous friction coefficient of the motor
E Counter-electromotive force
Ea Material elastic modulus
e Counter-electromotive force
ea, eb, ec Counter-electromotive force of each phase winding
F Constant force
f Sinusoidal response signal frequency
G (t) Intermediate function
G qs (s) Current-loop delay
G fe (s) Delay compensator
G ka (s) Position-loop delay
GLag (s) Improved Lagrange interpolation compensator
Go (z) Compensate function
Goc (s), Gcc (s) Transfer functions of the open and closed loops
Gop (s) Open-loop transfer function of the position-loop
G ope (s) Open-loop transfer function of the current-loop
G PIa (s) Position-loop controller
Gov (s) Open-loop transfer function of the velocity-loop
G PIb (s) Velocity-loop controller
G PIe (s) Current-loop controller
Gpr (s) Closed-loop transfer function of the position-loop
Gs (s) Transfer function
G sb (s) Velocity-loop delay
G se (s) Current-loop controlled object
GTrack Image tracker
GT (s) Electromagnetic torque
G ZOHa (s) Zero-order hold
Gθ (s) Controller of the position loop
Gω (s), GFF (s), GI (s) Controllers of the velocity loop
I Material cross-sectional moment of inertia
I (s) Current function
ia, ib, ic Current of each phase winding
id, iq Current of the dq axis
if Current
J Moment of inertia
K Free quantity
Ke Coefficient of counter-electromotive force
Kk Target manipulator deceleration ratio
Ko, Kδ, Kw Conversion coefficient
KN Coefficient of pulse conversion
KV, KI, Kω, Kθ Coefficients of visual servo
kP, kI, kD Coefficients of the velocity-loop controller
kp, ki, kd Coefficients of the current-loop controller
k pa, k ia, k qa Coefficients of the position-loop controller
L Stator inductance
L (x) Interpolation function
Ld, Lq Inductance of the dq axis
Ln (x) Lagrange interpolation polynomial
Lo Target manipulator rod length
l Length of the beam
li Length of the ith structure
Lk (x) Interpolation basis function
M Coil mutual inductance of each phase winding
M (x) The function of bending moment
Ma Bending moment
M × N Resolution
{mn, mn+1, ..., mn+4} Dataset of miss distance
N Interpolation step size
n Stepper motor speed
n0 Speed output of the decelerator
OA Line of sight line
OB Fire line
Oa, Ob Reference axis of the calibration tool
P The stress on the beam
Pn Number of motor poles
q Uniformly distributed load
R Stator resistance
s Differential module
S Tracking distance
ds Differential arc of a cantilever beam
T Sampling time
T1 Unit step signal duration
T2 Sinusoidal response signal duration
Td, Tf, Tk, Tb Constant of delay time
Te, TL Electromagnetic torque and load torque
Ts Sampling period of the encoder
t Unit step signal starting time
U (s) Voltage function
u Voltage
ua, ub, uc Voltage of each phase winding
ud, uq Voltage of the dq axis
v Linear velocity
w Deflection curve
wmax Maximum deflection
X (t) Sampling value
x Displacement
x0, x1, ..., xn Independent variables
Y Feedback coordinate
y0, y1, ..., yn Function values
ya1 Section deflection
yα1, yα2, yα3, yα4 Deflection distance
yβ1, yβ2, yβ3, yβ4 Total deflection distance
z A variable after the difference transformation
α Adjustment coefficient
β1, β2 Orthogonal decomposition distances
ψ Total flux of each phase winding
ψa, ψb, ψc Flux linkage of each phase winding
ψf Magnetic linkage of the permanent magnet
ψm Rotor permanent magnet flux
ψ sejθ s Flux components of each phase winding
θ Offset angle
θ* Output control instruction
θα1(lx) Offset angle of section A
θe Relative angle
θmax Maximum offset angle
ρ Curvature radius
δ1, δ3 Calibration deviation
δ2 Vertical distance
δmax Calibration error
|δ|, δx, δy Shooting accuracy judgment threshold
|δ| Tracking error
ε Stability coefficient of the closed loop
τI Integral time constant of the velocity loop
τi Integral time constant of the current loop
ω Target manipulator angular velocity
ωe Electrical angular velocity
ωr Angular velocity
(x, y) Target pixel coordinate
x, Δy) Pixel distance
(θM, θN) Angle of the lens
(θx, θy) Miss distance
(α, β) Output pulse of the motor
(w/2,h/2) Center of the lens

Acknowledgements

The present work was funded by the National Natural Science Foundation of China (Grant No. U19A2072), the Provincial Department of Education Postgraduate Scientific Research Innovation Project of Hunan Province of China (Grant No. QL20210007), and the Ministerial Level Postgraduate Funding Project of China (Grant No. JY2021A007).

Conflict of Interest

The authors declare that they have no conflict of interest.
1
Mazurkiewicz A. The Russia-Ukraine war of 2022: faces of modern conflict. Journal of Contemporary European Studies, 2023, 31(4): 1507–1508

DOI

2
Hu Q R, Shen X Y, Qian X M, Huang G Y, Yuan M Q. The personal protective equipment (PPE) based on individual combat: a systematic review and trend analysis. Defence Technology, 2023, 28: 195–221

DOI

3
Huang Q H, Jin G W, Xiong X, Ye H, Xie Y Z. Monitoring urban change in conflict from the perspective of optical and SAR satellites: the case of Mariupol, a city in the conflict between RUS and UKR. Remote Sensing, 2023, 15(12): 3096

DOI

4
Alhaji Musa S, Raja Abdullah R S A, Sali A, Ismail A, Abdul Rashid N E. Low-slow-small (LSS) target detection based on micro Doppler analysis in forward scattering radar geometry. Sensors, 2019, 19(15): 3332

DOI

5
Rasol J, Xu Y L, Zhang Z X, Zhang F, Feng W J, Dong L H, Hui T, Tao C Y. An adaptive adversarial patch-generating algorithm for defending against the intelligent low, slow, and small target. Remote Sensing, 2023, 15(5): 1439

DOI

6
Chang Y L, Li D, Gao Y L, Su Y, Jia X Q. An improved YOLO model for UAV fuzzy small target image detection. Applied Sciences, 2023, 13(9): 5409

DOI

7
Lin D, Wu Y M. Tracing and implementation of IMM Kalman filtering feed-forward compensation technology based on neural network. Optik, 2020, 202: 163574

DOI

8
Lu Y F, Fan D P, Zhang Z Y. Theoretical and experimental determination of bandwidth for a two-axis fast steering mirror. Optik, 2013, 124(16): 2443–2449

DOI

9
Han B, Wang H, Luo X, Liang C Y, Yang X, Liu S, Lin Y C. Turbidity-adaptive underwater image enhancement method using image fusion. Frontiers of Mechanical Engineering, 2022, 17(3): 13

DOI

10
Li H S, Zhang X Q. Three-dimensional coordinates test method with uncertain projectile proximity explosion position based on dynamic seven photoelectric detection screen. Defence Technology, 2022, 18(9): 1643–1652

DOI

11
Yin Y P, Zhao Y, Xiao Y G, Gao F. Footholds optimization for legged robots walking on complex terrain. Frontiers of Mechanical Engineering, 2023, 18(2): 26

DOI

12
Shen C, Wen Z J, Zhu W L, Fan D P, Chen Y K, Zhang Z. Prediction and control of small deviation in the time-delay of the image tracker in an intelligent electro-optical detection system. Actuators, 2023, 12(7): 296

DOI

13
Corriveau D. Validation of the NATO armaments ballistic Kernel for use in small-arms fire control systems. Defence Technology, 2017, 13(3): 188–199

DOI

14
Asad M, Khan S, Ihsanullah Z, Mehmood Y F, Shi S A, Memon U. A split target detection and tracking algorithm for ballistic missile tracking during the re-entry phase. Defence Technology, 2020, 16(6): 1142–1150

DOI

15
Liu H, Fan D P, Li S P, Zhou Q K. Design and analysis of a novel electric firing mechanism for sniper rifles. Acta Armamentarii, 2016, 37(6): 1111–1116

DOI

16
Chin K S H, Siu A C Y, Ying S Y K, Zhang Y F. Da jiang innovation, DJI: the future of possible. Academy of Asian Business Review, 2017, 3(2): 83–109

DOI

17
Sheu B H, Chiu C C, Lu W T, Huang C I, Chen W P. Development of UAV tracing and coordinate detection method using a dual-axis rotary platform for an anti-UAV system. Applied Sciences, 2019, 9(13): 2583

DOI

18
Huang D H, Zhou Z F, Zhang Z Z, Zhu M, Peng R W, Zhang Y, Li Q X, Xiao D N, Hu L W. Extraction of agricultural plastic film mulching in karst fragmented arable lands based on unmanned aerial vehicle visible light remote sensing. Journal of Applied Remote Sensing, 2022, 16(3): 036511

DOI

19
Liu Y, Sun P, Wergeles N, Shang Y. A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 2021, 172: 114602

DOI

20
Lyu M M, Liu R Z, Hou Y L, Gao Q, Wang L. A target motion filtering method for on-axis control of electro-optical tracking platform. Acta Armamentarii, 2019, 40(3): 548–554

DOI

21
Lyu M M, Hou R M, Ke Y F, Hou Y L. Compensation method for miss distance time-delay of electro-optical tracking platform. Journal of Xi’an jiaotong university, 2019, 53(11): 141–147

DOI

22
Wen Z J, Ding Y, Liu P K, Ding H. Direct integration method for time-delayed control of second-order dynamic systems. Journal of Dynamic Systems, Measurement, and Control, 2017, 139(6): 061001

DOI

23
Yoo S, Kim T, Seo M, Oh J, Kim H S, Seo T. Position-tracking control of dual-rope winch robot with rope slip compensation. IEEE/ASME Transactions on Mechatronics, 2021, 26(4): 1754–1762

DOI

24
Li J, Wang J Z, Wang S K. A novel method of fast dynamic optical image stabilization precision measurement based on CCD. Optik, 2011, 122(7): 582–585

DOI

25
Mondal A. Occluded object tracking using object-background prototypes and particle filter. Applied Intelligence, 2021, 51(8): 5259–5279

DOI

26
Tokuda F, Arai S, Kosuge K. Convolutional neural network-based visual servoing for eye-to-hand manipulator. IEEE Access, 2021, 9: 91820–91835

DOI

27
Yuan S S, Deng W X, Yao J Y, Yang G L. Robust adaptive precision motion control of tank horizontal stabilizer based on unknown actuator backlash compensation. Defence Technology, 2023, 20: 72–83

DOI

28
RafiqueM A, Lynch A F. Output-feedback image-based visual servoing for multirotor unmanned aerial vehicle line following. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(4): 3182–3196 10.1109/TAES.2020.2967851

29
Zhong H, Wang Y A, Miao Z Q, Li L, Fan S W, Zhang H. A homography-based visual servo control approach for an underactuated unmanned aerial vehicle in GPS-denied environment. IEEE Transactions on Intelligent Vehicles, 2023, 8(2): 1119–1129

DOI

30
Zhang K W, Shi Y, Sheng H Y. Robust nonlinear model predictive control based visual servoing of quadrotor UAVs. IEEE/ASME Transactions on Mechatronics, 2021, 26(2): 700–708

DOI

31
Bakthavatchalam M, Tahri O, Chaumette F. A direct dense visual servoing approach using photometric moments. IEEE Transactions on Robotics, 2018, 34(5): 1226–1239

DOI

32
Lin X K, Wang X, Li L. Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry. Applied Intelligence, 2020, 50(7): 2105–2119

DOI

Outlines

/