裴成禹

• 1:四川工商学院

摘要(Abstract)：

针对传统系统缺少最小方差估算步骤,容易受到信号干扰影响,存在预测精准度较低的问题,提出基于卡尔曼滤波的网球发球最佳击球点预测系统。根据系统硬件结构框图,设计预测感知模块,获取可读与不可读信息。为了使系统只传输可读信息,设计闭合开关,并在硬件末端设置客户端模块,显示预测结果,改善信号干扰问题。采用最小方差估计算法对硬件中的预测感知模块进行软件功能设计,并根据卡尔曼滤波原理进行多次迭代处理,获取最佳击球点滤波输出值。实验结果表明,该系统预测精准度最高可达到82%,能够准确找出最佳击球点。

关键词(KeyWords)： 卡尔曼滤波;网球;发球;最佳击球点;预测;滤波

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 裴成禹

DOI: 10.16652/j.issn.1004-373x.2018.11.036

参考文献(References)：

• [1]李春来,张海宁,杨立滨,等.基于无迹卡尔曼滤波神经网络的光伏发电预测[J].重庆大学学报,2017,40(4):54-61.LI Chunlai,ZHANG Haining,YANG Libin,et al.Photovoltaic power forecasting based on unscented Kalman filtering neural network[J].Journal of Chongqing University(natural science edition),2017,40(4):54-61.
• [2]刘鹏华,姚尧,梁昊,等.耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析[J].地球信息科学学报,2017,19(4):475-485.LIU Penghua,YAO Rao,LIANG Hao,et al.Analyzing spatiotemporal distribution of PM2.5 in China by integrating Kalman filter and multi-level clustering[J].Journal of geo-information science,2017,19(4):475-485.
• [3]辜小花,王坎,李燕,等.基于演化模型偏好多目标优化的智能采油辅助决策支持[J].机械工程学报,2017,53(13):159-169.GU Xiaohua,WANG Kan,LI Yan,et al.The intelligent oil extraction auxiliary decision based on evolution model and preference driven multi-objective optimization[J].Journal of mechanical engineering,2017,53(13):159-169.
• [4]张治远.基于三维图像的运动员起跑动作误差预测仿真[J].计算机仿真,2017,34(8):412-416.ZHANG Zhiyuan.Error prediction and simulation of athlete′s starting action based on 3D image[J].Computer simulation,2017,34(8):412-416.
• [5]杜正聪,邓寻.基于自适应遗传算法的粒子滤波器[J].成都理工大学学报(自然科学版),2017,44(5):636-640.DU Zhengcong,DENG Xun.Particle filter based on adaptive genetic algorithm[J].Journal of Chengdu University of Technology(science&technology edition),2017,44(5):636-640.
• [6]王坎,辜小花,高论,等.基于UPFNN的油田机采工艺动态演化建模[J].现代电子技术,2017,40(5):109-114.WANG Kan,GU Xiaohua,GAO Lun,et al.UPFNN-based dynamic evolution modeling of oil field mechanical plucking technology[J].Modern electronics technique,2017,40(5):109-114.
• [7]ZHENG Liang.Research on computer vision based trajectory tracking of rhythmic gymnastics[J].Modern electronics technique,2017,40(19):19-24.
• [8]彭继慎,吕东东,宋立业,等.基于非线性STUKF滤波器和ESN算法的耦合预测模型[J].传感技术学报,2017,30(7):1029-1034.PENG Jishen,LüDongdong,SONG Liye,et al.Prediction model of coupled algorithm based on nonlinear STUKF and ESN[J].Chinese journal of sensors and actuators,2017,30(7):1029-1034.
• [9]王炜,张国建,姜鸣,等.调度侧风电功率预测系统设计与应用[J].电网与清洁能源,2017,33(5):79-82.WANG Wei,ZHANG Guojian,JIANG Ming,et al.Design and application of wind power forecasting system at dispatching center[J].Advances of power system&hydroelectric engineering,2017,33(5):79-82.
• [10]高学攀,廖士中.基于贝叶斯网络的林火概率预测系统设计与实现[J].计算机工程与应用,2017,53(13):246-251.GAO Xuepan,LIAO Shizhong.Design and implementation of forest fire probability prediction system based on Bayesian network[J].Computer engineering and applications,2017,53(13):246-251.
• [11]施建俊,李庆亚,张琪,等.基于Matlab和BP神经网络的爆破振动预测系统[J].爆炸与冲击,2017,37(6):1087-1092.SHI Jianjun,LI Qingya,ZHANG Qi,et al.Forecast system for blasting vibration velocity peak based on Matlab and BP neural network[J].Explosion and shock waves,2017,37(6):1087-1092.
• [12]GUO Zhun,ZHOU Tianjun.The simulation of stratocumulus and its impacts on SST:based on the IAP near-term climate prediction system[J].Advances in earth science,2017,32(4):373-381.