Citation: | PANG Ce, SHAN Ganlin, DUAN Xiushenget al. Management method for multiple sensors' recognizing and tracking multiple targets cooperatively[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(8): 1674-1680. doi: 10.13700/j.bh.1001-5965.2018.0612(in Chinese) |
Aimed at the problem that there are false targets among targets being tracked, a target recognition model based on risk theory, Bayesian theory and evidence theory is established firstly. Secondly, the situation of target recognition when the target is being tracked is analyzed, and a risk function model in which both target tracking and recognition are considered at the same time is established. When calculating the sensor management, a distributed algorithm based on distributed computing of multi-Agent is proposed. The simulation experiment results show that:First, targets can be recognized effectively and the tracking progress ends in time once a target is recognized as a false target under the framework of target recognition in this paper; Second, the solution of the algorithm proposed in this paper is better and the calculation speed is faster than other algorithms; Third, the sensor management method in this paper can avoid the waste of sensor resources and improve the tracking effect of real targets.
[1] |
SUBEDI S, ZHANG Y D, AMIN M G, et al.Cramer-Rao type bounds for sparsity-aware multi-sensor multi-target tracking[J].Signal Processing, 2018, 145:68-77. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=16aa078d7cdbb8cca405f7425ffd3263
|
[2] |
ASGHAR A B, JAWAID S T, SMITH S L.A complete greedy algorithm for infinite-horizon sensor scheduling[J].Automatica, 2017, 81:335-341. doi: 10.1016/j.automatica.2017.04.018
|
[3] |
GOSTAR A, HOSEINNEZHAD R, WEIFEBG L, et al.Sensor-management for multi-target filters via minimization of posterior dispersion[J].IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6):2877-2884. doi: 10.1109/TAES.2017.2718280
|
[4] |
HONG H G, BA-NBGU T, MAULE R.The Cauchy-Schwarz divergence for poisson point process[J].IEEE Transactions on Information Theory, 2015, 61(8):4475-4485. doi: 10.1109/TIT.2015.2441709
|
[5] |
BUKAL M, MARKOVI I, PETROVI I.Score matching based assumed density filtering with the von Mises-Fisher distribution[C]//Proceedings of 20th International Conference on Information Fusion.Piscataway, NJ: IEEE Press, 2017: 433-438. https://lamor.fer.hr/images/50020776/Bukal2017.pdf
|
[6] |
SAYIN M O, LIN C W, SHIRAISH S, et al.Information-driven autonomous intersection control via incentive compatible mechanisms[J].IEEE Transactions on Intelligent Transportation Systems, 2018, 99(1):1-13. http://cn.bing.com/academic/profile?id=7181638afc19067717357a5c4c4bcf5f&encoded=0&v=paper_preview&mkt=zh-cn
|
[7] |
MARTIN S.Risk-based sensor resource management for field of view constraint sensor[C]//Proceedings of 18th International Conference on Information Fusion.Piscataway, NJ: IEEE Press, 2015: 2041-2048. https://c4i.gmu.edu/~pcosta/F15/data/fileserver/file/472057/filename/Paper_1570103675.pdf
|
[8] |
BORGES M E G, MALTESE D, VANHEEGHE P, et al.Sensor management using expected risk reduction approach[C]//Proceedings of 19th International Conference on Information Fusion, Piscataway, NJ: IEEE Press, 2016: 2050-2058. https://www.researchgate.net/publication/308697816_Sensor_Management_using_Expected_Risk_Reduction_approach
|
[9] |
BORGES M E G, MALTESE D, VANHEEGHE P, et al.A risk-based sensor management using random finite sets and POMDP[C]//Proceedings of 20th International Conference on Information Fusion.Piscataway, NJ: IEEE Press, 2017: 1588-1596. A risk-based sensor management using random finite sets and POMDP
|
[10] |
童俊, 单甘霖.基于修正Riccati方程和Kuhn-Munkres算法的多传感器跟踪资源分配[J].控制与决策, 2012, 27(5):747-751.
TONG J, SHAN G L.Study of multi-sensor allocation based on modified Riccati equation and Kuhn-Munkres algorithm[J].Control and Decision, 2012, 27(5):747-751(in Chinese).
|
[11] |
HEGARAT-MASCLE S L, BLOCH I, VIDAL-MADJAR D.Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing[J].IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(4):1018-1031. doi: 10.1109/36.602544
|
[12] |
HARE J Z, GUPTA S, WETTERGREN T A.POSE:Prediction-based opportunistic sensing for energy efficiency in sensor networks using distributed supervisors[J].IEEE Transactions on Cybernetics, 2017, 48(7):2114-2127. http://d.old.wanfangdata.com.cn/Periodical/yqyb201504003
|
[13] |
李志汇, 刘昌云, 倪鹏.反导多传感器协同任务规划综述[J].宇航学报, 2016, 37(1):29-38. doi: 10.3873/j.issn.1000-1328.2016.01.004
LI Z H, LIU C Y, NI P.Review on multisensor cooperative mission planning in anti-TBM system[J].Journal of Astronautics, 2016, 37(1):29-38(in Chinese). doi: 10.3873/j.issn.1000-1328.2016.01.004
|
[14] |
OZTURK O, BEGEN M A, ZARIC G S.A branch and bound algorithm for scheduling unit size jobs on parallel batching machines to minimize makespan[J].International Journal of Production Research, 2017, 55(6):1815-1831. doi: 10.1080/00207543.2016.1253889
|
[15] |
SHI C G, SALOUS S, WANG F, et al.Power allocation for target detection in radar networks based on low probability of intercept:A cooperative game theoretical strategy[J].Radio Science, 2017, 52(8):1030-1045. doi: 10.1002/2017RS006332
|
[16] |
ODEJIDE O O, BENTLEY E S, KONDI L P, et al.Effective resource management in visual sensor networks with MPSK[J].IEEE Signal Processing Letters, 2013, 20(8):739-742. doi: 10.1109/LSP.2013.2265912
|
[17] |
LIU Y, WANG H.UKF based nonlinear filtering using minimum entropy criterion[J].IEEE Transactions on Signal Processing Journal of Astronautics, 2013, 61(20):4988-4999. doi: 10.1109/TSP.2013.2274956
|