摘要
为提高基于压缩感知的无线传感器网络的定位精度和抗噪性,提出一种无线传感器网络下二进制鲸鱼优化算法压缩感知重构的多目标定位算法。首先将连续的鲸鱼优化算法离散在二进制空间中,并保留鲸鱼捕食策略的特性;再将二进制鲸鱼优化算法用于压缩感知信号重构;最终实现了无线传感器网络下的多目标定位。实验结果对比表明,相比于传统的压缩感知重构算法,该算法在目标数为8,信噪比为5 d B时,平均定位误差控制在1.25 m以内,具有良好的抗噪性,且计数性能和定位性能优于贪婪匹配追踪算法、传统的L1范数求解算法。
In order to improve the localization accuracy and anti-noise performance of wireless sensor network based on compressive sensing, a multi-target localization algorithm based on compressive sensing reconstruction of binary whale optimization algorithm in wireless sensor network is proposed. Firstly, the continuous whale optimization algorithm was discretized in the binary space, and the characteristics of whale preying strategy were retained. Then, the binary whale optimization algorithm was used to reconstruct the compressed sensing signal, and finally the multi-target positioning under the wireless sensor network was realized. The comparison of experimental results shows that compared with the traditional compressive sensing reconstruction algorithm, when the target number is 8 and the SNR is 5 dB, the average positioning error of this algorithm is controlled within 1.25 m and has good anti-noise performance. In addition, the counting performance and positioning performance are better than the greedy matching tracking algorithm and the traditional L1 norm solving algorithm.
作者
季章生
肖本贤
Ji Zhangsheng;Xiao Benxian(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2019年第11期102-109,共8页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(51577046)
国家自然科学基金重点项目(51637004)
国家重点研发计划“重大科学仪器设备开发”项目(2016YFF0102200)资助
关键词
压缩感知
二进制鲸鱼优化算法
多目标定位
捕食策略
compressive sensing
binary whale optimization algorithm
multi-target localization
preying strategy
作者简介
季章生,2017年于湖南科技大学获得学士学位,现为合肥工业大学硕士研究生,主要研究方向为压缩感知、无线传感器网络定位。E-mail:jzsl5197267965@126.com;肖本贤,分别在1986年、1989年和2004年于合肥工业大学获得学士学位、硕士学位和博士学位,现为合肥工业大学教授,主要研究方向为无线传感器网络、智能控制、系统建模。E-mail:xiaobenxian@126.com。
