基于映射曲线的自适应莱维鲸鱼无线定位算法

doi:10.13190/j.jbupt.2019-192

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (5): 125-129,136.doi: 10.13190/j.jbupt.2019-192

基于映射曲线的自适应莱维鲸鱼无线定位算法

余修武^1,2, 李莹¹, 刘永^1,2, 肖人榕¹, 余昊¹

1. 南华大学资源环境与安全工程学院, 衡阳 421001;
2. 湖南省铀尾矿库退役治理工程技术研究中心, 衡阳 421001

收稿日期:2019-09-19 发布日期:2021-03-11
通讯作者: 李莹(1996-),女,硕士生,E-mail:614151536@qq.com. E-mail:614151536@qq.com
作者简介:余修武(1976-),男,教授,硕士生导师.
基金资助:
国家自然科学基金项目（11875164）；湖南省重点研发计划项目（2018SK2055）

Wireless Localization Algorithm of Adaptive Levy Whale Based on Mapping Curve

YU Xiu-wu^1,2, LI Ying¹, LIU Yong^1,2, XIAO Ren-rong¹, YU Hao¹

1. School of Resource&Environment and Safety Engineering, University of South China, Hengyang 421001, China;
2. Hunan Province Engineering Research Center of Radioactive Control Technology in Uranium Mining and Metallurgy, Hengyang 421001, China

Received:2019-09-19 Published:2021-03-11

摘要/Abstract

摘要： 针对多维定标（MDS-MAP）算法计算效率低且定位精度不高的问题，提出了一种基于映射曲线的自适应莱维鲸鱼无线定位（AWL-MC）算法.采用映射曲线距离分析方法对待定位节点进行粗略相对定位，以提高节点的计算效率；再通过线性变换将相对坐标转换成绝对坐标；最后采用自适应莱维飞行鲸鱼优化算法对待定位节点坐标进行全局和局部搜索寻优处理，避免产生局部最优解，提高了定位精度.仿真结果表明，AWL-MC算法相比MDS-MAP算法的定位精度改进率为66.42%，计算效率提高了52.57%，相比多维定标扩展卡尔曼滤波的定位精度改进率为57.80%，计算效率提高了66.01%.

关键词: 无线传感器网络, 定位算法, 映射曲线, 莱维飞行鲸鱼算法

Abstract: Aiming at the problems of low calculation efficiency and low positioning accuracy of the multidimensional scaling map (MDS-MAP) algorithm, a wireless localization algorithm of adaptive Levy whale based on mapping curve (AWL-MC) is proposed. The mapping curve distance analysis method is used to make rough relative positioning of the localization nodes, so as to improve the calculation efficiency of nodes. Then the relative coordinates are converted into absolute coordinates by linear transformation. Finally, the adaptive Levy flight whale optimization algorithm is adopted to perform global and local search optimization processing for the coordinates of positioning nodes, so as to avoid local optimal solution and improve positioning accuracy. Simulations show that compared with MDS-MAP, AWL-MC algorithm has a 66.42% improvement rate in positioning accuracy and a 52.57% improvement in calculation efficiency. Compared with the multidimensional scaling extended Kalman filter, AWL-MC algorithm has a 57.80% improvement rate in positioning accuracy and a 66.01% improvement in calculation efficiency.

Key words: wireless sensor network, localization algorithm, mapping curve, Levy flight whale algorithm

中图分类号:

TP393

余修武, 李莹, 刘永, 肖人榕, 余昊. 基于映射曲线的自适应莱维鲸鱼无线定位算法[J]. 北京邮电大学学报, 2020, 43(5): 125-129,136.

YU Xiu-wu, LI Ying, LIU Yong, XIAO Ren-rong, YU Hao. Wireless Localization Algorithm of Adaptive Levy Whale Based on Mapping Curve[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 125-129,136.

参考文献

[1] 余修武, 刘琴, 李向阳, 等. 基于改进蚁群的BP神经网络WSN数据融合算法[J]. 北京邮电大学学报, 2018, 41(4):91-96. Yu Xiuwu, Liu Qin, Li Xiangyang, et al. Information fusion algorithm based on improved ant colony optimization BP neural network in WSN[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(4):91-96.
[2] Yu Xiuwu, Zhou Lixing, Li Xiangyang. A novel hybrid localization scheme for deep mine based on wheelgraph and chicken swarm optimization[J]. Computer Networks, 2019, (154):73-78.
[3] 田洪亮, 钱志鸿, 王义君, 等. 能量分簇传感器网络距离误差校正MDS-MAP定位算法[J]. 电子与信息学报, 2017, 39(7):1735-1740. Tian Hongliang, Qian Zhihong, Wang Yijun, et al. Modified MDS-MAP localization algorithm with distance error correction in energy clustering wireless sensor networks[J]. Journal of Electronics & Information Technology, 2017, 39(7):1735-1740.
[4] 李卫华, 贾丹, 王鹏. 基于不完全测距的移动传感器网络定位算法[J]. 控制与决策, 2018, 33(4):607-613. Li Weihua, Jia Dan, Wang Peng. Range-based localization algorithm for mobile sensor network with incomplete measurement[J]. Control and Decision, 2018, 33(4):607-613.
[5] 方旭明. 基于自适应卡尔曼滤波器的WSN定位算法研究[D]. 南京:南京大学, 2017.
[6] 陈岁生, 卢建刚, 楼晓春. 基于MDS-MAP和非线性滤波的WSN定位算法[J]. 浙江大学学报(工学版), 2012, 46(5):866-872. Chen Suisheng, Lu Jiangang, Lou Xiaochun. Localization algorithm for wireless sensor networks based on MDS-MAP and nonlinear filtering[J]. Journal of Zhejiang University (Engineering Science), 2012, 46(5):866-872.
[7] Li Li, Kunz T. Cooperative node localization using nonlinear data protection[J]. ACM Transactions on Sensor Networks, 2009, 5(1):1-26.
[8] 龙文, 蔡绍洪, 焦建军, 等. 求解大规模优化问题的改进鲸鱼优化算法[J]. 系统工程理论与实践, 2017, 37(11):2893-2994. Long Wen, Cai Shaohong, Jiao Jianjun, et al. Improved whale optimization algorithm for large scale optimization problems[J]. Systems Engineering-Theory & Practice, 2017, 37(11):2893-2994.
[9] 刘浩然, 张力悦, 范瑞星, 等. 基于改进鲸鱼优化策略的贝叶斯网络结构学习算法[J]. 电子与信息学报, 2019, 41(6):1434-1441. Liu Haoran, Zhang Liyue, Fan Ruixing, et al. Bayesian network structure learning based on improved whale optimization strategy[J]. Journal of Electronics & Information Technology, 2019, 41(6):1434-1441.
[10] 褚鼎立, 陈红, 王旭光. 基于自适应权重和模拟退火的鲸鱼优化算法[J]. 电子学报, 2019, 47(5):992-999. Chu Dingli, Chen Hong, Wang Xuguang. Whale optimization algorithm based on adaptive weight and simulated annealing[J]. Acta Electronica Sinica, 2019, 47(5):992-999.
[11] 钱志鸿, 孙大洋, Leung Victor. 无线网络定位综述[J]. 计算机学报, 2016, 39(6):1237-1256. Qian Zhihong, Sun Dayang, Leung Victor. A survey on localization model in wireless network[J]. Journal of Computer Science, 2016, 39(6):1237-1256.
[12] Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95:51-67.

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基于映射曲线的自适应莱维鲸鱼无线定位算法

Wireless Localization Algorithm of Adaptive Levy Whale Based on Mapping Curve

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