Advertisement

Cooperative Tracking Technology of Single Target Multi-Sensor Based on Cooperative Index

  • Jian-Wen GongEmail author
  • Xiao-Feng Wang
  • Ri-Jie Yang
Article
  • 1 Downloads

Abstract

The multi-sensor cooperative tracking problem of single machine moving target is studied in this paper. At present, the uncertainties of covariance and unreasonable setting of expectation threshold in cooperative tracking algorithm are easy to cause frequent switching of radar. Moreover, the existing sensor management is carried out under the same tracking accuracy, which does not meet the needs of the actual battlefield environment. To solve these problems, a multi-sensor cooperation algorithm based on information increment and covariance is proposed. Firstly, the expected threshold of cooperative tracking is revised by using co-variance and information increment. Then, the tracking accuracy of the tracked target is adaptively divided according to the battlefield situation. Finally, the high-precision cooperative tracking of single target multi-sensor is realized by the co-index. Simulation results have verified the effectiveness of the proposed algorithm. Compared with traditional algorithms, proposed algorithm not only solves the frequent problem of radar switch, but also saves more sensor resources and improves the combat and anti-destruction capability of aircraft.

Keywords

Collaborative tracking Information increment Covariance Synergy index 

Notes

References

  1. 1.
    Tharmarasa R, Kirubarajan T, Sinha A et al (2011) Decentralized Sensor Selection for Large-Scale Multisensor-Multitarget Tracking. IEEE Transactions on Aerospace & Electronic Systems 47(2):1307–1324CrossRefGoogle Scholar
  2. 2.
    Kolba MP, Scott WR, Collins LM (2011) A Framework for Information-Based Sensor Management for the Detection of Static Targets. IEEE Trans Syst Man Cybern Syst Hum 41(1):105–120CrossRefGoogle Scholar
  3. 3.
    Wu WH, Jiang J, Gao L (2015) Tracking maneuvering target in clutter with passive sensor aided by airborne radar. Control and Decision 30(2):277–228Google Scholar
  4. 4.
    Zhang Z, Shan G (2015) Non- myopic sensor scheduling to track multiple reactive targets. IET Signal Processing 9(1):37–47MathSciNetCrossRefGoogle Scholar
  5. 5.
    Shan GL, Zhang ZN (2014) Non- myopic sensor scheduling in a single platform for target tracking. Systems Engineering and Electronics 36(3):458–463Google Scholar
  6. 6.
    Shan G, Zhang Z (2017) Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP. Trans Inst Meas Control 39(2):230–243CrossRefGoogle Scholar
  7. 7.
    Kalandros M (2002) Covariance control for multisensor systems. IEEE Trans Aerosp Electron Syst 38(4):1138–1157CrossRefGoogle Scholar
  8. 8.
    Qiao CL, Duan XS, Shan GL (2018) Scheduling algorithm for multi-sensor collaboration tracking and radiation control. Journal of Beijing University of Aeronautics and Astronautics 44(7):1472–1480Google Scholar
  9. 9.
    Zhao M, Zuo Y, Ming-Di LI et al (2017) Distributed Multi-sensor Target Collaborative Tracking Algorithm under Communication Constraints. Fire Control & Command Control 42(6):6–9Google Scholar
  10. 10.
    SUNBERG Z, CHAKRAVORTY S, ERWIN RS (2016) Information space receding horizon control for multisensor tasking problem. IEEE Transactions on Cybernetics 46(6):1325–1336CrossRefGoogle Scholar
  11. 11.
    Liu S, Bai W, Liu G et al (2018) Parallel Fractal Compression Method for Big Video Data. Complexity 2018:1–16zbMATHGoogle Scholar
  12. 12.
    Kreucher CM, Hero AO, Kastella KD et al (2007) An Information-Based Approach to Sensor Management in Large Dynamic Networks. Proc IEEE 95(5):978–999CrossRefGoogle Scholar
  13. 13.
    Aughenbaugh JM, Lacour BR (2011) Sensor Management for Particle Filter Tracking. IEEE Transactions on Aerospace & Electronic Systems 47(1):503–523CrossRefGoogle Scholar
  14. 14.
    Khezri S, Faez K, Osmani A (2010) Modified Discrete Binary PSO Based Sensor Placement in WSN Networks. International Conference on Computational Intelligence and Communication Networks. IEEE Computer Society, 200–204Google Scholar
  15. 15.
    Liu S, Liu G, Zhou H A Robust Parallel Object Tracking Method for Illumination Variations. Mobile Networks and Applications.  https://doi.org/10.1007/s11036-018-1134-8
  16. 16.
    Qin L, Zheng L, Liu YF et al (2013) Maneuvering target collaborative tracking algorithm with multi-sensor deployment optimization. Systems Engineering & Electronics 35(2):304–309Google Scholar
  17. 17.
    Li SZ, Wang GH, Wei WU et al (2012) Detection and tracking technique for radar network in the presence of distributed jamming. Systems Engineering & Electronics 34(4):782–788Google Scholar
  18. 18.
    Wu W, Liu Y, Yang YS et al (2011) The Study on Air-borne Multi-sensor Synergy Tracking and Radiation Control. Journal of Projectiles, Rockets, Missiles and Guidance 31(1):153–156Google Scholar
  19. 19.
    Wu W, Wang GH, Liu Y et al (2011) Airborne radar/IRST/SEM synergistic tracking and management. System Engineering and Electronics 33(7):1517–1522Google Scholar
  20. 20.
    Liu S, Pan Z, Cheng X (2017) A Novel Fast Fractal Image Compression Method based on Distance Clustering in High Dimensional Sphere Surface. Fractals 25(4):1740004CrossRefGoogle Scholar
  21. 21.
    Song H, Xiao M, Xiao J et al (2016) A POMDP approach for scheduling the usage of airborne electronic countermeasures in air operations. Aerosp Sci Technol 48:86–93CrossRefGoogle Scholar
  22. 22.
    Roy A, Mitra D (2016) Unscented-Kalman-filter-based multitarget tracking algorithms for airborne surveillance application. J Guid Control Dyn 39(9):1949–1966CrossRefGoogle Scholar
  23. 23.
    Sehmaedeke W, Kastella K (1998) Information based sensor management and IMMKF. Proc SPIE 3373:390–401CrossRefGoogle Scholar
  24. 24.
    She J, Wang F, Zhou J (2016) A novel sensor selection and power allocation algorithm for multiple-target tracking in an LPI radar network. Sensors 16(12):2193–2206CrossRefGoogle Scholar
  25. 25.
    Huber MF (2012) Optimal pruning for multi- step sensor scheduling. IEEE Trans Autom Control 57(5):1338–1343MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Krishnamurthy V (2005) Emission management for low probability intercept sensors in network centric warfare. IEEE Trans Aerosp Electron Syst 41(1):133–151CrossRefGoogle Scholar
  27. 27.
    S Liu S, Fu W, He L et al (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl 76(4):5787–5802CrossRefGoogle Scholar
  28. 28.
    Li Y, Krakow LW, Chong EKP et al (2009) Approximate stochastic dynamic programming for sensor scheduling to track multiple targets. Digital Signal Processing 19(6):978–989CrossRefGoogle Scholar
  29. 29.
    Zhang Z, Shan G (2014) UTS-based foresight optimization of sensor scheduling for low interception risk tracking. International Journal of Adaptive Control and Signal Processing 28(10):921–931CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Naval Aviation UniversityYantaiChina
  2. 2.Aviation University of Air ForceChangchunChina

Personalised recommendations