复合K噪声下机动目标跟踪自适应UPF算法

doi:10.3969/j.issn.0372-2112.2012.06.029

电子学报

复合K噪声下机动目标跟踪自适应UPF算法

刘望生^1,2, 李亚安¹, 王明环³

1. 浙江理工大学机械与自动控制学院,浙江杭州 310012;
2. 西北工业大学航海学院,陕西西安 710072;
3. 浙江工业大学特种装备制造与先进加工技术教育部重点实验室,浙江杭州 310012

收稿日期:2011-02-16 修回日期:2011-09-19 出版日期:2012-06-25
- 作者简介:
- 刘望生 男,1974年1月出生于湖北省洪湖市.现为西北工业大学博士后.主要研究方向为机动目标跟踪、数据融合. E-mail:lwsh22@hotmail.com
  李亚安 男,1961年6月出生于陕西省周至市.现为西北工业大学教授、博士生导师.学科研究方向为:目标跟踪、水声信号处理、多传感器信息处理.主持国家自然科学基金、国防基金等多项基金项目的研究.在国内外核心刊物上发表学术论文30余篇,其中EI检索8篇. E-mail:liyaan@nwpu.edu.cn
- 基金资助:
- 国家自然科学基金 (No.51179158,No.51179157,No.50905165); 浙江省特种装备制作与先进加工技术重点实验室开放基金 (No.2011EM010)

An Adaptive UPF Algorithm for Tracking Maneuvering Target in Compound K Noise Environment

LIU Wang-sheng^1,2, LI Ya-an¹, WANG Ming-huan³

1. School of Mechanical Engineering and Antomation,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China;
2. College of Marine,Northwestern Polytechnical University,Xi'an,Shaanxi 710072,China;
3. Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education,Zhejiang University of Technology,Hangzhou,Zhejiang 310012,China

Received:2011-02-16 Revised:2011-09-19 Online:2012-06-25 Published:2012-06-25
- Supported by:
- National Natural Science Foundation of China (No.51179158, No.51179157, No.50905165); Zhejiang Provincial Open Fund for Key Laboratory of Special Purpose Equipment and Advanced Processing Technology (No.2011EM010)

摘要/Abstract

摘要： 针对复合K噪声下机动目标跟踪系统具有强非线性非高斯的特点,提出了一种自适应无迹粒子滤波(Adaptive Unscented Particle Filter,AUPF)算法.该算法建立在常加速模型及其改进滤波算法基础上,并将无迹卡尔曼滤波(Unscented Kalman Filter,UKF)与强跟踪滤波(Strong Tracking Filter,STF)算法相结合作为提议分布,提高了系统跟踪一般机动和阶跃机动的能力.在给出复合K噪声模型的基础上,利用AUPF算法对几种典型机动目标进行了计算机仿真,并同无迹粒子滤波(Unscented Particle Filter,UPF)算法进行了比较.仿真结果表明,复合K噪声下AUPF算法能更有效地对各种机动目标进行跟踪,具有较高的跟踪精度.

关键词: 机动目标, 常加速模型, AUPF算法, 强跟踪滤波, 复合K噪声

Abstract: Aimed at the strong nonlinear and non-Gaussian characteristics of maneuvering target tracking system under compound K noise,an adaptive unscented particle filter (AUPF) algorithm is proposed.Based on constant acceleration (CA) model and its modified filtering algorithm,the algorithm adopts a new proposal distribution which combines unscented Kalman filter (UKF) and strong tracking filter (STF) and enhances the system performance for tracking general mobile and step mobile.The AUPF algorithm is applied to track several kinds of typical maneuvering targets based on the model of compound K noise.And the comparison with the unscented particle filter (UPF) algorithm is given.The simulation results show that AUPF algorithm has good track performance for tracking various maneuvering targets and has high tracking precision.

Key words: maneuvering target, constant acceleration model, AUPF algorithm, strong tracking filter, compound K noise

中图分类号:

TN953

刘望生, 李亚安, 王明环. 复合K噪声下机动目标跟踪自适应UPF算法[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2012.06.029.

LIU Wang-sheng, LI Ya-an, WANG Ming-huan . An Adaptive UPF Algorithm for Tracking Maneuvering Target in Compound K Noise Environment[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2012.06.029.

参考文献

[1] Abraham D A,Lyons A P.Simulation of non-Rayleigh reverberation and clutter[J].IEEE Journal of Oceanic Engineering,2004,29(2):347-362.

[2] Blom H A P,Bloem E A.Bayesian tracking of two possibly unresolved maneuvering targets[J].IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):612-627.

[3] Blom H A P,Bloem E A.Exact Bayesian filter and joint IMM coupled PDA tracking of maneuvering targets from possibly missing and false measurements[J].Automatica,2006,42(1):127-135.

[4] Bar-Shalom Y,Chen H.IMM estimator with out-of-sequence measurements .Proceedings of SPIE-The International Society for Optical Engineering .Orlando:SPIE,2004.192-201.

[5] Li X R,Jilkov V P.Survey of maneuvering target tracking PartⅤ:Multiple-model methods[J].IEEE Transactions on Aerospace and Electronic Systems,2005,41(4):1255-1321.

[6] Kim H S,Park J G,Lee D.Adaptive fuzzy IMM algorithm for uncertain target tracking[J].International Journal of Control,Automation,and Systems,2009,7(6):1001-1008.

[7] Lan J,Mu C.Input-adaptive models based multiple-model algorithm for maneuvering target tracking[J].Chinese Journal of Electronics,2009,18(1):84-88.

[8] Liang Y,Zhou D H,Zhang L,et al.Adaptive filtering for stochastic systems with generalized disturbance inputs[J].IEEE Signal Processing Letters,2008,15(2008):645-648.

[9] Arulampalam M S,Maskell S,Gordon N,et al.A tutorial on particle filters for online nonlinear/non-Gaussian Bayesial tracking[J].IEEE Transactions Signal Processing,2002,50(2):174-188.

[10] Bugallo M F,Xu S S,Djuric P M.Performance comparison of EKF and particle filtering methods for maneuvering targets[J].Digital Signal Processing:A Review Journal,2007,17(4):774-786.

[11] Van Der Merwe R,Doucet A,De Freitas N,et al.The Unscented Particle Filter .Cambridge:Cambridge University Engineering Department,2000.

[12] 常发亮,马丽,刘增晓,等.复杂环境下基于自适应粒子滤波器的目标跟踪[J].电子学报,2006,34(12):2150-2153. Chang Fa-liang,Ma Li,Liu Zeng-xiao,et al.Target tracking based on adaptive particle filter under complex background[J].Acta Electronica Sinica,2006,34(12):2150-2153.(in Chinese)

[13] 郭荣华,覃征,李国兵,等.一种带重采样控制器的IMM-AUPF算法[J].清华大学学报(自然科学版),2010,50(4):539-542. Guo R H,Qin Z,Li G B,et al.An IMM-AUPF algorithm with a resampling controller .Journal of Tsinghua University (Sci & Tech),2010,50(4):539-542.(in Chinese)

[14] Du S C,Shi Z G,Zang W,et al.Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler[J].Journal of Zhejiang University Science A,2007,8(8):1277-1282.

[15] Shi Z G,Hong S H,Chen K S.Tracking airborne targets hidden in blind Doppler using current statistical model particle filter[J].Progress in Electromagnetics Research,2008,82(2008):227-240.

[16] 潘泉,梁彦,杨峰,等.现代目标跟踪与信息融合[M].北京:国防工业出版社,2009.35-39. Pan Quan,Liang Yan,Yang Feng,et al.Modern Target Tracking and Information Fusion[M].Beijing:National Defence Industry Press,2009.35-39.(in Chinese)

[17] 潘平俊,冯新喜,李锋,等.基于强跟踪滤波器的自适应常加速模型及跟踪算法[J].系统工程理论与实践,2008,24(5):136-147. Pan Ping-jun,Feng Xin-xi,Li Feng,et al.Adaptive constant acceleration model and tracking algorithm based on strong tracking filter[J].Systems Engineering-Theory & Practice,2008,24(5):136-147.(in Chinese)

[18] Yang Yuan-xi,Gao Wei-guang.A new learning statistic for adaptive filter based on predicted residuals[J].Progress in Natural Science,2006,16(8):833-837.

[19] 周东华,叶银忠.现代故障诊断与容错控制[M].北京:清华大学出版社,2000:265-267. Zhou Dong-hua,Ye Yin-zhong.Modern Fault Diagnosis and Fault Tolerant Control .Beijing:Tsinghua University Press,2000:265-267.(in Chinese)

[20] 文成林,周东华.多尺度估计理论及其应用[M].北京:清华大学出版社,2002.251-256. Wen Cheng-lin,Zhou Dong-hua.Multiscale Estimate Theory and Application[M].Beijing:Tsinghua University Press,2002.251-256.(in Chinese)

[21] 范小军,刘锋,秦勇,等.基于STF的"当前"统计模型及自适应跟踪算法[J].电子学报,2006,34(6):981-984. Fan Xiao-jun,Liu Feng,Qin Yong,et al.Current statistic model and adaptive tracking algorithm based on strong tracking filter[J].Acta Electronica Sinica,2006,34(6):981-984.(in Chinese)

[22] Abraham D A,Lyons A P.Novel physical interpretations of K-distributed reverberation[J].IEEE Journal of Oceanic Engineering,2002,27(4):800-813.

[23] 李国鸿,梁红.一种K分布随机数产生方法[J].系统仿真学报,2007,19(2):448-449. Li Guo-hong,Liang Hong.Generator of K-Distributed random number[J].Journal of System Simulation,2007,19(2):448-449.(in Chinese)

复合K噪声下机动目标跟踪自适应UPF算法

An Adaptive UPF Algorithm for Tracking Maneuvering Target in Compound K Noise Environment

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	叶瑾, 许枫, 杨娟, 王佳维. 一种基于多传感器的复合量测IMM-EKF数据融合算法[J]. 电子学报, 2020, 48(12): 2326-2330.
[2]	王树亮, 毕大平, 阮怀林, 杜明洋, 潘继飞. 基于信息熵准则的认知雷达机动目标跟踪算法[J]. 电子学报, 2019, 47(6): 1277-1284.
[3]	李姝怡, 程婷. 基于量测转换IMM的多普勒雷达机动目标跟踪[J]. 电子学报, 2019, 47(3): 538-544.
[4]	张浩为, 谢军伟, 葛佳昂, 宗彬锋, 路文龙. 基于波形调度的机动目标跟踪算法[J]. 电子学报, 2019, 47(3): 560-567.
[5]	贺雄鹏, 廖桂生, 许京伟, 魏嘉琪. 基于频率轴反转的机动目标距离徙动补偿方法[J]. 电子学报, 2018, 46(6): 1496-1502.
[6]	李东, 占木杨, 粟嘉, 方志平, 曹海林. 一种基于相干积累CPF和NUFFT的机动目标ISAR成像新方法[J]. 电子学报, 2017, 45(9): 2225-2232.
[7]	许登荣, 程水英, 包守亮. 自适应转移概率交互式多模型跟踪算法[J]. 电子学报, 2017, 45(9): 2113-2120.
[8]	张翔宇, 王国宏, 宋振宇, 张静. LFM雷达对临近空间高超声速目标的跟踪研究[J]. 电子学报, 2016, 44(4): 846-853.
[9]	徐树生, 林孝工, 李新飞. 强跟踪自适应平方根容积卡尔曼滤波算法[J]. 电子学报, 2014, 42(12): 2394-2400.
[10]	商临峰, 杨小军, 邢科义. 局域跟踪的测速雷达网量测融合弹道解算[J]. 电子学报, 2013, 41(3): 615-618.
[11]	文韬, 葛泉波. 未建模系统基于观测值的实时分块Kalman滤波估计方法研究[J]. 电子学报, 2012, 40(10): 1958-1964.
[12]	张冬晨;王东进;陈卫东. 基于联合互时频分布的InISAR三维成像方法[J]. 电子学报, 2009, 37(4): 833-838.
[13]	闫莉萍;邓志红;付梦印. 非线性系统的异步多速率数据融合估计算法研究[J]. 电子学报, 2009, 37(12): 2735-2740.
[14]	盛琥;杨景曙;曾芳玲;张帆. 引入输入估计的交互式多模跟踪算法[J]. 电子学报, 2009, 37(12): 2810-2814.
[15]	马海平;陈子栋. 一种基于H_∞滤波的模糊变结构交互多模型算法[J]. 电子学报, 2008, 36(2): 245-249.