电光与控制, 2016, 23 (5): 1, 网络出版: 2016-06-06   

复合K噪声下目标跟踪的改进粒子滤波算法研究

Target Tracking Based on Modified Particle Filter Algorithm in Compound K Noise Environment
作者单位
火箭军工程大学自动化系, 西安 710025
摘要
针对复合K噪声干扰下目标跟踪系统中出现的强非线性非高斯问题, 在给出一种复合K噪声统计模型的基础上, 提出将容积粒子滤波(CPF)与无迹粒子滤波(UPF)两种算法应用在典型目标跟踪系统中, 并对算法的跟踪性能进行了仿真分析。实验结果表明, CPF, UPF两种算法均能有效跟踪复合K噪声下的运动目标;其中, CPF算法表现出更高的跟踪精度和更好的实时性, 且具有更低的算法设计复杂度。
Abstract
Aimed at the strong nonlinear and non-Gaussian problems of target tracking system under compound K noise jamming, the Cubature Particle Filter (CPF) algorithm and Unscented Particle Filter (UPF) algorithm are applied in typical target tracking systems based on the compound K noise statistical model, and the tracking performance of the algorithm are analyzed through simulation.The experimental results illustrate that:1) Both the CPF and UPF algorithms have good performance in tracking moving target under condition with compound K noise;and 2) Compared with UPF, the CPF has higher tracking accuracy, better real-time performance, and lower complexity in the algorithm design.
参考文献

[1] ARASARATNAM I, HAYKIN S.Cubature Kalman filters[J].IEEE Transactions on Automatic Control, 2009, 54(6):1254-1269.

[2] 蔡宗平, 戴定成, 牛创, 等.基于Sage-Husa的优化粒子滤波算法[J].电光与控制, 2015, 22(1):16-19.(CAI Z P, DAI D C, NIU C, et al.An optimized particle filtering algorithm based on Sage-Husa[J].Electronics Optics & Control, 2015, 22(1):16-19.)

[3] DE FREITAS J F G, NIRANJAN M, GEE A H, et al.Sequential Monte Carlo methods to train neural network models[J].Neural Computation, 2000, 12(4):955-993.

[4] VAN DER MERWE R, DOUCET A, DE FREITAS N, et al.The unscented particle filter[Z].Cambridge:Cambridge University Engineering Department, 2000.

[5] 孙枫, 唐李军.Cubature 粒子滤波[J].系统工程与电子技术, 2011, 33(11):2554-2557.(SUN F, TANG L J.Cu-bature particle filter[J].Systems Engineering and Electronics, 2011, 33(11):2554-2557.)

[6] 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, 2012, 40(6):1240-1245.(LIU W S, LI Y A, WANG M H.An adaptive UPF algorithm for tracking maneuvering target in compound K noise environment[J].Acta Electronica Sinica, 2012, 40(6):1240-1245.)

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

[8] 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, 452.(LI G H, LIANG H.Generator of K-distributed random number[J].Journal of System Simulation, 2007, 19(2):448-449, 452.)

[9] 戴定成, 蔡宗平, 牛创.基于简化平方根容积卡尔曼滤波的跟踪算法[J].电光与控制, 2015, 22(3):11-14.(DAI D C, CAI Z P, NIU C.Target tracking algorithm based on reduced square-root cubature Kalman filter [J].Electronics Optics & Control, 2015, 22(3):11-14.)

[10] 孙枫, 唐李军.Cubature 卡尔曼滤波与 Unscented 卡尔曼滤波估计精度比较[J].控制与决策, 2013, 28(2):303-308.(SUN F, TANG L J.Estimation precision comparison of Cubature Kalman filter and Unscented Kalman filter[J].Control and Decision, 2013, 28(2):303-308.)

蔡宗平, 牛创, 张雪影, 戴定成, 朱斌. 复合K噪声下目标跟踪的改进粒子滤波算法研究[J]. 电光与控制, 2016, 23(5): 1. CAI Zong-ping, NIU Chuang, ZHANG Xue-ying, DAI Ding-cheng, ZHU Bin. Target Tracking Based on Modified Particle Filter Algorithm in Compound K Noise Environment[J]. Electronics Optics & Control, 2016, 23(5): 1.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!