期刊文献+

强干扰环境下性能优化的相互作用多模型-概率数据关联算法 被引量:3

THE ADVANCED IMM-PDAF ALGORITHM IN A HEAVY CLUTTER ENVIRONMENT
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摘要 在大区域空中交通管制 ATM(Air Traffic Manage)调度多批次飞机时 ,由于各种干扰和虚警的存在 ,致使相互作用多模型 -概率数据关联算法 (IMM- PDA)的性能下降 .为此 ,提出一个新颖的算法 ,即利用某些先验概率知识构造一个判断回波有效性的函数 ,通过该函数来估计无效回波、并将其排除在外 ,从而改善了算法的性能 .最后 ,通过仿真给以验证 . The performance of IMM-PDA algorithm can be decreased due to all kinds of clutter and false alarms during managing lots of planes in large region air traffic manage ment(ATM). Hence, a novel algorithm is presented in this paper, that is, a function appraising the performance of IMM-PDA is set up with some prior probabilities measurements caused by clutter or other targets can be estimated efficiently and filtered out via this method, Then the performance of IMM-PDA is improved. At last, simulation for verification of the algorithm is given.
出处 《自动化学报》 EI CSCD 北大核心 2001年第2期267-271,共5页 Acta Automatica Sinica
基金 国家自然科学基金重点资助项目! ( 69732 0 1 0 )
关键词 空中交通管制 性能优化 状态方程 IMM-PDA算法 ATM(Air Traffic Manage),multitarget tracking,IMM-PDA(Interact Multiple Mode-Probability Data Association) algorithm.
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参考文献1

  • 1Bar Shalom Y,Multitarget Multisensor Tracking:Principles and Techniques,1995年,95页

同被引文献24

  • 1McGinnity S,Irwin G W.Multiple model bootstrap fil-ter for maneuvering target tracking.IEEE Transactions on Aerospace and Electronic Systems,2000,36(3):1006-1012.
  • 2Gordon N J,Maskell S,Kirubarajan T.Efficient particle fil-ters for joint tracking and classification.In:Proceedings of the Signal and Data Processing of Small Targets.Orlando,USA:SPIE,2002.439-449.
  • 3Pollard E,Pannetier B,Rombaut M.Hybrid algorithms for multitarget tracking using MHT and GM-CPHD.IEEE Transactions on Aerospace and Electronic Systems,2011,47(2):832-847.
  • 4Mahler R.Multitarget Bayes filtering via first-order multi-target moments.IEEE Transactions on Aerospace and Elec-tronic Systems,2003,39(4):1152-1178.
  • 5Vo B N,Ma W K.The Gaussian mixture probability hy-pothesis density filter.IEEE Transactions on Signal Pro-cessing,2006,54 (11):4091-4104.
  • 6Li W,Jia Y M.Gaussian mixture PHD filter for jump Markov models based on best-fitting Gaussian approxima-tion.Signal Processing,2011,91(4):1036-1042.
  • 7Liu W F,Han C Z,Lian F,Zhu H Y.Multitarget state ex-traction for the PHD filter using MCMC approach.IEEE Transactions on Aerospace and Electronic Systems,2010,46(2):864-883.
  • 8Pasha S A,Vo B N,Tuan H D,Ma W K.A Gaussian mixture PHD filter for jump Markov system models.IEEE Transac-tions on Aerospace and Electronic Systems,2009,45(3):919-936.
  • 9Clark D,Vo B N.Convergence analysis of the Gaussian mix-ture PHD filter.IEEE Transactions on Signal Processing,2007,55 (4):1204-1212.
  • 10Punithakumar K,Kirubarajan T,Sinha A.Multiple-model probability hypothesis density filter for tracking maneuver-ing targets.IEEE Transactions on Aerospace and Electronic Systems,2008,44(1):87-98.

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