An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering 被引量：1

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

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摘要 We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance. We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli （SMC-MB） filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets. Since most clutter measurements do not participate in the update step, the computing time is reduced significantly. Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

作者 Tong-yang JIANG Mei-qin LIU Xie WANG Sen-lin ZHANG

机构地区 State Key Laboratory of Industrial Control Technology College of Electrical Engineering

出处《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2014年第6期445-457,共13页 浙江大学学报C辑（计算机与电子（英文版）

基金 Project supported by the National Natural Science Foundationof China(Nos.61174142,61222310,and 61374021) the Specialized Research Fund for the Doctoral Program of Higher Education of China(Nos.20120101110115 and 20130101110109) theZhejiang Provincial Science and Technology Planning Projects ofChina(No.2012C21044) the Marine Interdisciplinary ResearchGuiding Funds for Zhejiang University(No.2012HY009B) theFundamental Research Funds for the Central Universities(No.2014XZZX003-12) the Aeronautical Science Foundation ofChina(No.20132076002)

关键词 Measurement-driven Gating technique Sequential Monte Carlo Multi-Bernoulli filter Multi-target filtering Measurement-driven, Gating technique, Sequential Monte Carlo, Multi-Bernoulli filter, Multi-target filtering

分类号 TN713 [电子电信—电路与系统]

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1Bar-Shalom, Y., Li, X.R., Kirubarajan, T., 2001. Estimation with Applications to Tracking and Navigation. Wiley, Ncw York.
2Baser, E., Kirubarajan, T., Ere, M., 2013. Improved MeMBer filter with modeling of spurious targets. Int. Conf. on Information Fusion, p.813-819.
3Blackman, S., Popoli, R., 1999. Design and Analysis of Modern Tracking Systems. Artech House, Norwood.
4Gostar, A.K., Hoscinnezhad, R., Bab-Hadiashar, A., 2013a. Multi-Bernoulli sensor control for multi-target tracking. Proc. IEEE 8th Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, p.312- 317. Idol: 10.1109/ISSNIP.2013.6529808].
5Gostar, A.K., Hoseinnezhad, R., Bab-Hadiashar, A., 2013b. Robust multi-Bernoulli sensor selection for multi-targettracking in sensor networks. IEEE Trans. Signal Pro- Lett., 20(12):1167-1170. [doi:lO.1109/LSP.2013. 2283735].
6j Hoang, H.G., Vo, B.T., 2014. Sensor management for multi-target tracking via multi-Bernoulli filtering. Au- tomatica, 50(4):1135-1142. [doi:10.1016/j.automatica. 2014.02.007].
7Hoseinnezhad, R., Vo, B.N., Vo, B.T., et al., 2011. Bayesian integration of audio and visual information for multi- target tracking using a CB-MeMBer filter. IEEE Int. Conf. on Acoust Speech Signal Processing, p.2300-2303. [doff 10.1109/ICASSP.2011.5946942].
8Hoseinnezhad, R., Vo, B.N., Vo, B.T., 2013. Visual tracking in background subtracted image sequences via multi- Bernoulli filtering. IEEE Trans. Signal Process., 61(2):392-397. [doi: 10. l109/TSP.2012.2222389].
9Li, X.R., Jilkov, V., 2003. Survey of maneuvering target tracking. IEEE Trans. Aerosp. Electron. Syst., 39(4):1333-1364. [doi: 10.1109/TAES.2003.1261132].
10Lian, F., Li, C., Han, C.Z., et al., 2012. Con- vergence analysis for the SMC-MeMBer and SMC- CBMeMBer filters. J. Appl. Math., Article ID 584140. [doi:10.1155/2012/584140].