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联合弹性碰撞与梯度追踪的WSNs压缩感知重构

刘洲洲 李士宁 王皓 张倩昀

刘洲洲, 李士宁, 王皓, 张倩昀. 联合弹性碰撞与梯度追踪的WSNs压缩感知重构. 自动化学报, 2020, 46(1): 178-192. doi: 10.16383/j.aas.c170241
引用本文: 刘洲洲, 李士宁, 王皓, 张倩昀. 联合弹性碰撞与梯度追踪的WSNs压缩感知重构. 自动化学报, 2020, 46(1): 178-192. doi: 10.16383/j.aas.c170241
LIU Zhou-Zhou, LI Shi-Ning, WANG Hao, ZHANG Qian-Yun. A Compressed Sensing Reconstruction Based on Elastic Collision and Gradient Pursuit Strategy for WSNs. ACTA AUTOMATICA SINICA, 2020, 46(1): 178-192. doi: 10.16383/j.aas.c170241
Citation: LIU Zhou-Zhou, LI Shi-Ning, WANG Hao, ZHANG Qian-Yun. A Compressed Sensing Reconstruction Based on Elastic Collision and Gradient Pursuit Strategy for WSNs. ACTA AUTOMATICA SINICA, 2020, 46(1): 178-192. doi: 10.16383/j.aas.c170241

联合弹性碰撞与梯度追踪的WSNs压缩感知重构

doi: 10.16383/j.aas.c170241
基金项目: 

国家自然科学基金 61871313

中国博士后科学基金 2018M633573

西安市科技计划项目 2017076CG/RC039 (XAHK001)

校级科研基金 2017KY1112

详细信息
    作者简介:

    李士宁  西北工业大学教授.主要研究方法为无线网络通信与安全, 大数据分析, 物联网, 智能计算. E-mail: lishining@nwpu.edu.cn

    王皓  挪威科技大学奥勒松校区工程与科学学院副教授. 2006年获得华南理工大学博士学位.主要研究方向为大数据, 物联网, 软件工程. E-mail: hawa@ntnu.no

    张倩昀  西安航空学院讲师. 2007获得南京航空航天大学硕士学位, 主要研究方向为信号处理. E-mail: jinganqy1988@126.com

    通讯作者:

    刘洲洲  西安航空学院教授. 2016年获得西北工业大学信息工程专业博士学位.主要研究方向为移动通信, 随机接入网络, 传感器器网络.本文通信作者. E-mail: nazi2005@126.com

A Compressed Sensing Reconstruction Based on Elastic Collision and Gradient Pursuit Strategy for WSNs

Funds: 

National Natural Science Foundation of China 61871313

China Postdoctoral Science Foundation Funded Project 2018M633573

Xi'an Science and Technology Project 2017076CG/RC039 (XAHK001)

School Level Scientiflc Research Fund 2017KY1112

More Information
    Author Bio:

    LI Shi-Ning  Professor at Northwestern Polytechnical University, China. His research interest covers wireless communication and security, big data analytics, industrial internet of things, and intelligent computing

    WANG Hao  Associate professor at Natural Sciences in Norwegian University of Science & Technology, Norway. He received his Ph. D. degree from South China University of Technology, China in 2006. His research interest covers wireless communication and security, big data analytics, and industrial internet of things, high performance computing, and safety-critical systems

    ZHANG Qian-Yun  Lecturer at Xi'an Aeronautical University, China. She received her bachelor from Nanchang Hangkong University, China in 2007. Her main research interest is signal processing

    Corresponding author: LIU Zhou-Zhou  Professor at Xi'an Aeronautical University, China. He received his Ph. D. degree in information engineering from Northwestern Polytechnical University, China in 2016. His main research interest covers mobile communications, random access in mobile radio networks, sensor networks. Corresponding author of this paper
  • 摘要: 为提高压缩感知(Compressed sensing, CS)大规模稀疏信号重构精度, 提出了一种联合弹性碰撞优化与改进梯度追踪的WSNs (Wireless sensor networks)压缩感知重构算法.首先, 创新地提出一种全新的智能优化算法---弹性碰撞优化算法(Elastic collision optimization algorithm, ECO), ECO模拟物理碰撞信息交互过程, 利用自身历史最优解和种群最优解指导进化方向, 并且个体以N(0, 1)概率形式散落于种群最优解周围, 在有效提升收敛速度的同时扩展了个体搜索空间, 理论定性分析表明ECO依概率1收敛于全局最优解, 而种群多样性指标分析证明了算法全局寻优能力.其次, 针对贪婪重构算法高维稀疏信号重构效率低、稀疏度事先设定的缺陷, 在设计重构有效性指数的基础上将ECO应用于压缩感知重构算法中, 并引入拟牛顿梯度追踪策略, 从而实现对大规模稀疏度未知数据的准确重构.最后, 利用多维测试函数和WSNs数据采集环境进行仿真, 仿真结果表明, ECO在收敛精度和成功率上具有一定优势, 而且相比于其他重构算法, 高维稀疏信号重构结果明显改善.
    Recommended by Associate Editor YANG Jian
    1)  本文责任编委  杨健
  • 图  1  极值碰撞示意图

    Fig.  1  Extreme value collision schematic diagram

    图  2  改进StOMP贪婪重构算法实现

    Fig.  2  Improved StOMP greedy reconstruction algorithm implementation

    图  3  不同$ n $下最优解均值和平均运行时间变化曲线

    Fig.  3  Mean value and average run time curve of optimal solution under different $ n $

    图  4  不同$ \varepsilon $下最优解均值和平均运行时间变化曲线

    Fig.  4  The mean value and average run time curve of optimal solution under different $ \varepsilon $

    图  5  不同$ \xi $下最优解均值和平均运行时间变化曲线

    Fig.  5  The mean value and average run time curve of optimal solution under different $ \xi $

    图  6  不同$ \xi $下最优解均值和平均运行时间变化曲线

    Fig.  6  The mean value and average run time curve of optimal solution under different $ \beta $

    图  7  4种不同优化算法函数收敛曲线

    Fig.  7  Function convergence curves of four different optimization algorithms

    图  8  不同$ K $取值下$ \left({\tau, t_{s} } \right) $变化情况

    Fig.  8  Changes in different $ K $ values $ \left({\tau, t_{s} } \right) $

    图  9  4种重构算法稀疏信号重构结果对比

    Fig.  9  Comparison of four reconstruction algorithms for sparse signal reconstruction results

    图  10  稀疏度$ K $对重构结果影响

    Fig.  10  The influence of the sparsity $K$ on the reconstruction results

    图  11  测量数目$ M $对重构结果影响

    Fig.  11  The influence of the number of measurements $M$ on the reconfiguration results

    图  12  不同算法抗噪声能力干扰对比

    Fig.  12  Interference contrast of anti noise ability of different algorithms

    表  1  基准测试函数

    Table  1  Benchmark functions

    函数名称 目标函数 维数 取值范围
    Scaffer $ \begin{aligned} f_{1} \left(x \right)= 0.5-\frac{[{\sin^{2}\left({x_{1}^{2} +x_{2}^{2} } \right)^{0.5}}]}{[{1+0.001\left({x_{1}^{2} +x_{2}^{2} } \right)^{2}}]} \end{aligned} $ 2 [0, 1]
    Sphere $ f_{2} \left(x \right)=\sum\limits_{i=1}^n {x_{i}^{2} } $ 5 $ (-30, 30) $
    Griewank $ \begin{aligned} f_{3} \left(x \right)=\frac{1}{4000}\sum\limits_{i=1}^n {x_{i}^{2} } - \prod\limits_{i=1}^n {\cos \left({\frac{x_{i} }{\sqrt{i}}} \right)} +1 \end{aligned} $ 20 $ (-30, 30) $
    Scaffer7 $ \begin{aligned} f_{4} \left(x \right)=\sum\limits_{i=1}^{n-1} {\left({x_{i}^{2} +x_{i+1}^{2} } \right)^{0.25}} \times [{\sin^{2}({50\left({x_{i}^{2} +x_{i+1}^{2} } \right)^{0.1}})+2}] \end{aligned} $ 30 $ (-100, 100) $
    Rastrigin $ \begin{aligned} f_{5} \left(x \right)= \sum\limits_{i=1}^n {({x_{i}^{2} -10\cos 2\pi x_{i} +10})} \end{aligned} $ $ n $ $ (-5.12, 5.12) $
    Rosenrrock $ \begin{aligned} f_{6} \left(x \right)= \sum\limits_{i=1}^n {[{100\left({x_{i+1} -x_{i}^{2} } \right)^{2}+x_{i}^{2} }]} \end{aligned} $ $ n $ $ (-30, 30) $
    下载: 导出CSV

    表  2  不同函数收敛性能指标对比结果

    Table  2  Comparison results of convergence performance indexes of different functions

    $ f $ 算法 $ Su $ (%) $ Max $ $ Min $ $ \overline {Ave} $ $ T$ (s)
    $ f_{1} $ ECO 100 -0.997 -1 -0.999 6.79
    PSO 12 -0.23 -0.95 -0.68 11.37
    DSA 100 -0.96 -1 -0.98 6.13
    SWPA 100 -0.93 -1 -0.97 7.74
    $ f_{2} $ ECO 100 1.78E-6 0 6.37E-10 6.37
    PSO 100 5.27E-4 5.77E-6 1.19E-4 12.76
    DSA 100 6.27E-3 3.09E-4 1.62E-3 8.36
    SWPA 100 1.04E-4 3.33E-5 6.24E-5 14.55
    $ f_{3} $ ECO 100 7.24E-4 0 1.29E-3 10.88
    PSO 0 1.51 0.012 0.84 15.27
    DSA 98 3.28E-3 1.93E-3 2.74E-3 12.70
    SWPA 100 6.53E-3 2.85E-3 3.48E-3 11.09
    $ f_{4} $ ECO 100 2.81E-5 6.34E-8 7.11E-7 22.16
    PSO 0 17.11 6.25 10.78 27.94
    DSA 79 0.07 1.83E-2 0.04 11.22
    SWPA 80 0.18 3.64E-72 0.01 14.55
    $ f_{5} $ ECO 100 1.11E-6 0 4.89E-9 7.41
    PSO 0 20.47 11.25 19.86 10.28
    DSA 65 0.063 3.21E-2 0.017 6.89
    SWPA 86 0.025 4.44E-3 0.008 8.77
    $ f_{6} $ ECO 17 0.467 0.013 0.227 15.23
    PSO 0 88.171 21.073 44.110 20.79
    DSA 0 22.145 6.667 8.936 18.81
    SWPA 11 6.652 2.147 3.667 12.56
    下载: 导出CSV

    表  3  不同重构评价指标对比

    Table  3  Comparison of evaluation indexes of different reconfiguration

    重构算法稀疏度 $ K=15 $
    MSE DSR (%) $ \overline {T_{l} } $ (s) SNR
    IStOMP 1.23 100 14.881 39.114
    StOMP 91.27 7 9.524 27.047
    SP 12.54 89 6.227 36.541
    BSMP 16.76 99 8.123 35.046
    下载: 导出CSV
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  • 收稿日期:  2017-05-05
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