基于信道状态信息的船舶驾驶员值班行为识别方法

陈嘉鸣; 刘克中; 陈默子; 马杰; 曾旭明

doi:10.16411/j.cnki.issn1006-7736.2020.03.008

大连海事大学学报 >

2020 , Vol. 46 >Issue 3: 68 - 75

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2020.03.008

交通运输工程

基于信道状态信息的船舶驾驶员值班行为识别方法

陈嘉鸣 ,
刘克中 ,
陈默子 ,
马杰 ,
曾旭明

展开

(武汉理工大学 a.航运学院；b.内河航运技术湖北省重点实验室，武汉 430063)

陈嘉鸣（1996 — ），男，硕士生，E-mail：chenjiaming@whut.edu.cn.

收稿日期: 2020-04-26

修回日期: 2020-07-20

网络出版日期: 2020-07-20

基金资助

国家自然科学基金资助项目（51979216）；湖北省技术创新专项重大项目（2017AAA120）.

收起

Recognition method of ship’s officer duty behavior based on channel state information

CHEN Jia-ming ,
LIU Ke-zhong ,
CHEN Mo-zi ,
MA Jie ,
ZENG Xu-ming

Expand

(a.School of Navigation;b.Hubei Provincial Key Laboratory of Inland Navigation Technology,Wuhan University of Technology, Wuhan 430063,China)

Received date: 2020-04-26

Revised date: 2020-07-20

Online published: 2020-07-20

Fold

摘要

为更加高效智能地识别出驾驶员值班行为特征，提出一种基于Wi-Fi信道状态信息(CSI)的驾驶员值班行为识别方法.在分析驾驶员值班过程中不同行为对CSI时域和频域影响的基础上，通过提取CSI中表征不同值班行为对应的速度特征，构建包括驾驶员静止、行走、原地操作活动三种不同值班状态的隐马尔可夫模型(HMM)，利用HMM对人体行为特征序列进行判别，从而实现驾驶员值班行为状态的分类.实船测试表明，该方法具有较高的准确性和鲁棒性，识别不同值班状态的平均准确度可达90.3%，可为智能航运监管、船舶航行安全提供帮助.

关键词： 船舶; 值班驾驶员(OOW); 行为识别; 隐马尔可夫模型(HMM); 信道状态信息(CSI)

本文引用格式

陈嘉鸣 , 刘克中 , 陈默子 , 马杰 , 曾旭明 . 基于信道状态信息的船舶驾驶员值班行为识别方法[J]. 大连海事大学学报, 2020 , 46(3) : 68 -75 . DOI: 10.16411/j.cnki.issn1006-7736.2020.03.008

Abstract

In order to identify the characteristics of OOW’s watch-keeping behavior more efficiently and intelligently, a method of watch-keeping behavior recognition based on Wi-Fi channel state information (CSI) was proposed. Based on the analysis of the impact of different behaviors on the CSI time and frequency domain during OOW’s watch-keeping process, by extracting the speed characteristics corresponding to different behaviors from CSI, a hidden Markov model (HMM) including three different duty states of OOW as stationary, walking and in situ operation was constructed, and HMM was used to distinguish the human behavior feature sequence to realize the classification of OOW on duty behavior. The test on board proves that the proposed method has higher accuracy and robustness, and the average identifying accuracy of different duty states can reach 90.3%, which can provide help for intelligent shipping supervision and ship navigation safety.

Key words： ship; officer on watch(OOW); behavior recognition; hidden Markov model(HMM); channel state information(CSI)

参考文献

[1] 严新平. 智能船舶的研究现状与发展趋势[J]. 交通与港航, 2016, 3(1): 23-26.
YAN X P. Research status and development trend of intelligent ships[J]. Communication & Shipping, 2016, 3(1): 23-26. (in Chinese)
[2] 吴兆麟, 朱军. 海上交通工程（第二版）[M]. 大连: 大连海事大学出版社, 2004.
WU Z L, ZHU J. Marine traffic engineering (the second edition)[M]. Dalian: Dalian Maritime University Press, 2004. (in Chinese)
[3] JIA J, PENG H, RUAN N, et al. WiFind: Driver fatigue detection with fine-grained Wi-Fi signal features[J]. IEEE Transactions on Big Data, 2018, 6(1): 1086-1096.
[4] KUGE N, YAMAMUR T, SHIMOYAMA O, et al. A driver behavior recognition method based on a driver model framework[J]. Intelligent Vehicle Systems, 2010, 25(4): 69-76.
[5] 徐小丽. 视觉特征对船舶驾驶行为的影响研究[D]. 武汉: 武汉理工大学, 2012.
XU X L. Research on the influence of visual characteristics on ship driving behavior[D]. Wuhan: Wuhan University of Technology, 2012. (in Chinese)
[6] 席永涛, 胡甚平, 陈伟炯, 等. 船舶驾驶员操作可靠性量化的CREAM改进模型[J]. 中国安全科学学报, 2015, 25(11): 3-9.
XI Y T, HU S P, CHEN W J, et al. A modified CREAM model for ship OOW operation reliability[J]. China Safety Science Journal, 2015, 25(11): 3-9. (in Chinese)
[7] 鲁勇, 吕绍和, 王晓东, 等. 基于WiFi信号的人体行为感知技术研究综述[J]. 计算机学报, 2019, 42(2): 231-251.
LV Y, LV S H, WANG X D, et al. A Survey on WiFi Based Human Behavior Analysis Technology[J]. Chinese Journal of Computers, 2019, 42(2): 231-251. (in Chinese)
[8] YANG Z, ZHOU Z M, LIU Y H. From RSSI to CSI: Indoor Localization via Channel Response[J]. Acm Computing Surveys, 2013, 46(2):1-32.
[9] CHEN M Z, LIU K Z, MA J, et al. SWIM: Speed-aware WiFi-based Passive Indoor Localization for Mobile Ship Environment[J]. IEEE Transactions on Mobile Computing, 2019, 10(2): 977-990.
[10] 陈家豪, 刘克中, 陈默子, 等. 基于信道状态信息的船舶敏感区域入侵检测方法[J]. 大连海事大学学报, 2018, 45(1): 89-95.
CHEN J H, LIU K Z,CHEN M Z, et al. Intrusion detection method of ships sensitive regions based on channel state information[J]. Journal of Dalian Maritime University, 2018, 45(1): 89-95. (in Chinese)
[11] LIU K Z, CHEN M Z, CAI E. et al. Indoor localization strategy based on fault-tolerant area division for shipboard surveillance [J]. Automation in Construction, 2018, 67(12): 206-218.
[12] WANG W, LIU A X, SHAHZAD M, et al. Device-free human activity recognition using commercial Wi-Fi devices[J]. Journal on Selected Areas in Communications, 2017, 35(1): 1135-1148.
[13] 陈伟炯. 船舶安全与管理[M]. 大连: 大连海事大学出版社, 1998.
CHEN W J. Ship safety and management[M]. Dalian: Dalian Maritime University Press, 1998. (in Chinese)
[14] 贺炎, 王斌, 王忠民. 小波分解在移动用户行为识别中的应用[J]. 北京邮电大学学报, 2016, 39(04): 67-70.
HE Y, WANG B, WANG Z M. Application of Wavelet Decomposition in Mobile User Behavior Recognition[J]. Journal of Beijing University of Posts and Telecommunications, 2016, 39(04): 67-70. (in Chinese)
[15] Deng Q, Wang J, SOFFKER D, Prediction of human driver behaviors based on an improved HMM approach[C]// IEEE Intelligent Vehicles Symposium , 2018: 2066-2071.
[16] CHEN J X, LIU Z Y. Study on solitary word based on HMM model and Baum-Welch algorithm[J]. Journal of Hebei University of Science and Technology, 2017, 36(1): 52-57.

Options

文章导航

模态框（Modal）标题

摘要

本文引用格式

Abstract

参考文献