计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 318-322.

• 网络与通信 • 上一篇    下一篇

基于IRWQS与模糊特征的位置预测算法

陈波,张云贺,邱少明,王运明   

  1. 大连大学信息工程学院 辽宁 大连116622
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:陈 波(1972-),男,博士,教授,CCF会员,主要研究方向为一体化指挥控制网络技术;张云贺(1990-),男,硕士生,主要研究方向为网络接入控制,E-mail:zhangyunhe666@foxmail.com(通信作者);邱少明(1977-),男,副教授,CCF会员,主要研究方向为高速信号采集与处理;王运明(1987-),男,博士生,CCF会员,主要研究方向为指挥控制网络理论。
  • 基金资助:
    装备发展部预研基金项目(6140130101,61400010301)资助

Position Prediction Algorithm Based on IRWQS and Fuzzy Features

CHEN Bo,ZHANG Yun-he, QIU Shao-ming, WANG Yun-ming   

  1. School of Information Engineering,Dalian University,Dalian,Liaoning 116622,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 针对现有二维位置预测算法难以反映地势因素给预测准确度带来的影响,提出一种基于IRWQS(Incremental Repetition Weighing Queue Strategy)与模糊特征相结合的位置预测方法。首先,将从北斗卫星导航系统获取的三维位置坐标信息进行提取转换后存入数据库,再利用数据库的链式操作进行在线增量式重复加权队列扫描运算;其次,通过模糊特征匹配算法获取最优的位置坐标,并得出较为准确的下一运动位置坐标点以及运动趋势。实验结果表明,相比MMTS算法和UCMBS算法,所提算法的预测准确率分别平均提高约9%和25%。

关键词: IRWQS, 模糊特征, 三维位置坐标信息, 位置预测

Abstract: In view of the fact that the existing two-dimensional position prediction algorithm is difficult to reflect the influence of terrain factors on prediction accuracy,this paper proposed a position prediction algorithm based on IRWQS (Incremental Repetition Weighing Queue Strategy) and fuzzy feature.Firstly,the three-dimensional position coordinate information obtained from the Plough satellite navigation system is extracted and converted into a database,and then the online incremental weighting queue scan operation is performed by using the chained operation of the database.Secondly,the optimal position coordinates are obtained through the fuzzy feature matching algorithm to get the coordinate points and movement trends of the next moving position exactly.The experimental results show that compared with MMTS algorithm and UCMBS algorithm,the prediction accuracy of this algorithm increases by about 9% and 25% on average.

Key words: Fuzzy feature, IRWQS, Position prediction, Three-dimensional position coordinate information

中图分类号: 

  • TP393.0
[1]卓永宁,苏冰,王昭.DTN中一种基于灰色预测和状态估计的位置预测方法[J].计算机应用,2017,34(4):1162-1165.
[2]REZA A T,KUMAR T A,SIVAKUMAR T.Position Prediction based Multicast Routing (PPMR) using Kalman Filter over VANET[C]∥IEEE International Conference on Engineering and Technology.IEEE,2016:198-206.
[3]REZA A T,KUMAR T A,SIVAKUMAR T.Position Prediction based Multicast Routing (PPMR) using Kalman Filter over VANET[C]∥IEEE International Conference on Engineering and Technology.IEEE,2016:198-206.
[4]林树宽,李昇智,乔建忠,等.基于用户移动行为相似性聚类的Markov位置预测[J].东北大学学报(自然科学版),2016,37(3):323-326.
[5]WU E,ZHANG P,LU T,et al.Behavior prediction using an improved Hidden Markov Model to support people with disabilities in smart homes[C]∥IEEE,International Conference on Computer Supported Cooperative Work in Design.IEEE,2016:560-565.
[6]ZOU Y,ZHANG S.Position Prediction Social-Relationship-Based on Multi-order Markov Model[C]∥Third International Conference on Advanced Cloud and Big Data.IEEE,2016:36-43.
[7]LI T,PRIETO J,CORCHADO J M.Fitting for smoothing:A methodology for continuous-time target track estimation[C]∥International Conference on Indoor Positioning and Indoor Navigation.IEEE,2016:1-8.
[8]任东宇,任东旭,张元.基于UWB和Skyline的室内三维实时定位技术研究[J].地理空间信息,2017,15(8):113-115.
[9]张国川.基于北斗卫星导航定位系统的三维地球综合应用系统[C]∥中国卫星应用大会会议.2012:3.
[10]余春红.基于优先队列的增量式重复记录识别[J].计算机应用,2003,23(9):61-63.
[11]彭艳兵,姚伟烈,刘卫江.基于地理位置时间序列的相似性研究[J].电子设计工程,2017,25(8):37-40.
[12]戚文博,张曦煌.基于混合相似度和信任传播的位置推荐系统[J].计算机应用与软件,2017,34(9):97-102,138.
[13]李斌,张博,刘学军,等.基于Jaccard相似度和位置行为的协同过滤推荐算法[J].计算机科学,2016,43(12):200-205.
[14]王凡,陈健.基于概念相似度计算的多策略本体映射研究[J].计算机技术与发展,2015,25(4):38-42,47.
[15]宋路杰,孟凡荣,袁冠.基于Markov模型与轨迹相似度的移动对象位置预测算法[J].计算机应用,2016,36(1):39-43.
[16]胡艳,朱晓瑛,马刚.基于K-Means和时间匹配的位置预测模型[J].郑州大学学报(工学版),2017,38(2):17-20.
[17]乔少杰,金琨,韩楠,等.一种基于高斯混合模型的轨迹预测算法[J].软件学报,2015,26(5):1048-1063.
[18]薛迪,吴礼发,李华波,等.TraDR:一种基于轨迹分解重构的移动社交网络位置预测方法[J].计算机科学,2016,43(3):93-98.
[19]乔少杰,李天瑞,韩楠,等.大数据环境下移动对象自适应轨迹预测模型[J].软件学报,2015,26(11):2869-2883.
[20]李婕,夏兴有,王兴伟,等.机会认知网络中基于社会关系的节点位置预测算法[J].东北大学学报(自然科学版),2014,35(12):1701-1705.
[21]李倩伟,唐丙寅.基于大数据分析的移动对象轨迹预测方法[J].计算机测量与控制,2016,24(10):198-201.
[1] 刘嘉琛, 秦小麟, 朱润泽.
基于LSTM-Attention的RFID移动对象位置预测
Prediction of RFID Mobile Object Location Based on LSTM-Attention
计算机科学, 2021, 48(3): 188-195. https://doi.org/10.11896/jsjkx.200600134
[2] 夏扬波, 杨文忠, 张振宇, 王庆鹏, 石研.
一种移动无线传感器网络的节点位置预测方法
Node Position Prediction Method for Mobile Wireless Sensor Networks
计算机科学, 2018, 45(8): 113-118. https://doi.org/10.11896/j.issn.1002-137X.2018.08.020
[3] 王振朝,侯欢欢,连蕊.
WSN中基于位置预测的地理路由算法
Geographic Routing Algorithm Based on Location Prediction in WSN
计算机科学, 2018, 45(5): 59-63. https://doi.org/10.11896/j.issn.1002-137X.2018.05.010
[4] 李姗姗,陈莉,张永新,袁娅婷.
基于RPCA的图像模糊边缘检测算法
Fuzzy Edge Detection Algorithm Based on RPCA
计算机科学, 2018, 45(5): 273-279. https://doi.org/10.11896/j.issn.1002-137X.2018.05.047
[5] 李昇智, 乔建忠, 林树宽.
一种基于用户移动行为相似性的位置预测方法
Location Prediction Method Based on Similarity of Users Moving Behavior
计算机科学, 2018, 45(12): 288-292. https://doi.org/10.11896/j.issn.1002-137X.2018.12.046
[6] 佟振明, 刘志鹏.
大型多人在线角色扮演游戏的下一地点预测
Next Place Prediction of Massively Multiplayer Online Role-playing Games
计算机科学, 2018, 45(11A): 453-457.
[7] 薛迪,吴礼发,李华波,洪征.
TraDR:一种基于轨迹分解重构的移动社交网络位置预测方法
TraDR:A Destination Prediction Method Based on Trajectory Decomposition and Reconstruction in Geo-social Networks
计算机科学, 2016, 43(3): 93-98. https://doi.org/10.11896/j.issn.1002-137X.2016.03.019
[8] 王梦冉,乔少杰,于珊珊.
蜂窝网中基于位置预测的切换算法
Handover Algorithm Based on Location Prediction in Cellular Network
计算机科学, 2014, 41(Z11): 187-190.
[9] 林新棋.
基于改进模糊综合评价的电影情感分类
Film Affective Classification Based on Improved Fuzzy Comprehensive Evaluation
计算机科学, 2014, 41(2): 161-165.
[10] .
一种新的遥感影像边缘检测方法

计算机科学, 2007, 34(7): 235-237.
[11] .
移动对象位置预测的索引方法

计算机科学, 2006, 33(8): 170-172.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!