基于改进果蝇优化算法正交匹配追踪的超声信号降噪方法

doi:10.12263/DZXB.20210515

电子学报 ›› 2022, Vol. 50 ›› Issue (2): 493-501.DOI: 10.12263/DZXB.20210515

基于改进果蝇优化算法正交匹配追踪的超声信号降噪方法

董明¹^,²^,³^,⁴, 李敬¹^,⁴, 索永录³, 唐恩贤², 马宏伟¹^,⁴, 陈渊⁴, 张广明⁵, 万翔¹^,⁴

^1.西安科技大学机械学院，陕西西安 710054
^2.陕西陕煤黄陵矿业集团有限责任公司技术中心，陕西延安 727307
^3.西安科技大学能源学院，陕西西安 710054
^4.陕西省矿山机电装备智能监测重点实验室，陕西西安 710054
^5.英国利物浦约翰摩尔斯大学通用工程研究所，英国利物浦 L3 3AF

收稿日期:2021-04-21 修回日期:2021-10-26 出版日期:2022-02-25 发布日期:2022-02-25
作者简介:董明男，1984年出生，湖北恩施人.现为西安科技大学副教授，硕士生导师.主要研究方向为超声无损检测与评价、机械测试理论与技术等.E-mail: jesunatg@hotmail.com
李敬男，1997年出生，陕西西安人.现为西安科技大学硕士研究生.主要研究方向为超声信号处理.E-mail: 328024342@qq.com
基金资助:
国家自然科学基金(51705418);中国博士后科学基金(2019M653696);陕西省自然科学基础研究计划陕煤联合基金(2021JLM-07);陕西省自然科学基础研究计划(2019JQ-801)

An Improved Fruit Fly Optimization Algorithm Based Orthogonal Matching Pursuit for Ultrasonic Noise Reduction

DONG Ming¹^,²^,³^,⁴, LI Jing¹^,⁴, SUO Yong-lu³, TANG En-xian², MA Hong-wei¹^,⁴, CHEN Yuan⁴, ZHANG Guang-ming⁵, WAN Xiang¹^,⁴

^1.School of Mechanical Engineering，Xi’an University of Science and Technology，Xi’an，Shaanxi 710054，China
^2.Technology Center，Shaanxi Huangling Coal Mine Co. ，Ltd. ，Yan’an，Shaanxi 727307，China
^3.School of Energy Engineering，Xi’an University of Science and Technology，Xi’an，Shaanxi 710054，China
^4.Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring，Xi’an，Shaanxi 710054，China
^5.General Engineering Research Institute，Liverpool John Moores University，Liverpool L3 3AF，United Kingdom

Received:2021-04-21 Revised:2021-10-26 Online:2022-02-25 Published:2022-02-25

摘要/Abstract

摘要：

降噪是超声信号处理的重要环节，正交匹配追踪是一种常用的降噪方法，传统正交匹配追踪算法计算量大、分解精度不高，无法提取强噪声背景下的超声信号.本文提出了一种结合果蝇优化算法和正交匹配追踪的超声信号降噪算法，将正交匹配追踪中的“贪婪”搜索转换为Gabor函数的参数优化问题，利用果蝇优化算法估计Gabor函数的最优值，采用自适应步长以提高果蝇优化算法的全局遍历性，同时引入高维广义CAT映射以跳出局部最优，最后由寻找到的最佳原子重构超声信号.为验证算法的有效性，对仿真的多频超声回波信号和实验采集的锻件试块超声回波信号进行了降噪处理，结果表明，本文提出的方法能有效提取强噪声背景下的超声信号.

关键词: 正交匹配追踪, 果蝇优化算法, 自适应步长, 高维广义CAT映射, 数字信号处理

Abstract:

Noise reduction is the key technology of ultrasonic testing whose performance affects the evaluation of ultrasonic testing results directly. Orthogonal matching pursuits(OMP) is one of the most popular methods for the purpose of noise reduction; however, OMP suffers from a number of disadvantages of high complexity and time-consuming of the atom searching, and it is unable to extract the ultrasonic signal under background of powerful noise. An improved fruit fly optimization algorithm based orthogonal matching pursuit(IFOA-OMP) for ultrasonic noise reduction method is proposed, the parameters of Gabor function are estimated by IFOA, and the global ergodicity is improved by the adaptive step function, at the same time, the high-dimensional extensive CAT map is introduced to jump out of the local optimum. Finally, the ultrasonic signal is reconstructed from the best matching atoms. In order to validate the effectiveness of the proposed method, the simulated multi frequency ultrasonic signal and the real ultrasonic signal of forging block are used. The processing results show the proposed method can accurately extract the ultrasonic signal under background of powerful noise.

Key words: orthogonal matching pursuit(OMP), fruit fly optimization algorithm(FOA), adaptive step, high-dimensional extensive CAT map, digital signal process

中图分类号:

TB553

董明, 李敬, 索永录, 唐恩贤, 马宏伟, 陈渊, 张广明, 万翔. 基于改进果蝇优化算法正交匹配追踪的超声信号降噪方法[J]. 电子学报, 2022, 50(2): 493-501.

DONG Ming, LI Jing, SUO Yong-lu, TANG En-xian, MA Hong-wei, CHEN Yuan, ZHANG Guang-ming, WAN Xiang. An Improved Fruit Fly Optimization Algorithm Based Orthogonal Matching Pursuit for Ultrasonic Noise Reduction[J]. Acta Electronica Sinica, 2022, 50(2): 493-501.

图/表 14

图1 自适应步长的演化过程

图2 不同原子搜索步长性能对比

图3 2种映射序列的遍历分布图

图4 2种映射序列的分布直方图

图5 改进的果蝇优化算法流程图

图6 仿真超声信号时域波形和频谱图

图7 不同降噪方法结果对比

图8 不同信噪比超声信号降噪效果对比

图9 实验超声信号

图10 IFOA-OMP处理结果

图11 低功率超声信号

图12 IFOA-OMP处理低功率超声信号结果

图13 加噪实验超声信号处理结果对比

表1 不同算法去噪效果

方法	均方根误差	输出信噪比
OMP	0.145 7	4.217 9
FOA-OMP	0.142 3	5.378 3
IFOA-OMP	0.138 4	5.612 1

参考文献 27

1	王喆, 杨辰龙, 周晓军, 等. 基于超声背散射信号递归定量分析的CFRP局部孔隙缺陷识别方法[J]. 振动与冲击, 2019, 38(21): 229-235.
	WANG Z, YANG C L, ZHOU X J, et al. Identification method for CFRP local pore defects based on recursive quantitative analysis of ultrasonic backscattering signal[J]. Journal of Vibration and Shock, 2019, 38(21): 229-235. (in Chinese)
2	王大为, 王召巴. 一种强噪声背景下微弱超声信号提取方法研究[J]. 物理学报, 2018, 67(21): 65-77.
	WANG D W, WANG Z B. Weak ultrasonic signal detection in strong noise[J]. Acta Physica Sinica, 2018, 67(21): 65-77. (in Chinese)
3	ZHANG G M, ZHANG C Z, HARVEY D M. Sparse signal representation and its applications in ultrasonic nde[J]. Ultrasonics, 2012, 52(3): 351-363.
4	SI D, GAO B, GUO W, et al. Variational mode decomposition linked wavelet method for emat denoise with large lift-off effect[J]. NDT and E International, 2019, 107(5): 102149.
5	HUGUES G D C, LUIS R C, JOSE A. Optical real-time Fourier transformation with kilohertz resolutions[J]. Optica, 2016, 3(1): 1-8.
6	LIU R X, SHU M L, CHEN C F. Ecg signal denoising and reconstruction based on basis pursuit[J]. Applied Sciences, 2021, 11(4): 1591.
7	董道广, 芮国胜, 田文飚, 等. 基于稀疏贝叶斯学习的时域流信号鲁棒动态压缩感知算法[J]. 电子学报, 2020, 48(5): 990-996.
	DONG D G, RUI G S, TIAN W B, et al. A robust dynamic compressive sensing algorithm for streaming signals in time domain based on sparse Bayesian learning[J]. Acta Electronica Sinica, 2020, 48(5): 990-996. (in Chinese)
8	陈建, 田野, 孙晓颖. 基于稀疏谱匹配的高分辨DOA估计方法[J]. 北京理工大学学报, 2016, 36(10): 1043-1047.
	CHEN J, TIAN Y, SUN X Y. High resolution direction-of-arrival estimation based on sparse spectral fitting[J]. Transactions of Beijing Institute of Technology, 2016, 36(10): 1043-1047. (in Chinese)
9	陈斌, 牛铜, 张连海, 等. 不相关匹配追踪的分段区分性特征变换方法[J]. 电子学报, 2016, 44(12): 2924-2931.
	CHEN B, NIU T, ZHANG L H, et al. A discriminative segmental feature transform method based on uncorrelated matching pursuit[J]. Acta Electronica Sinica, 2016, 44(12): 2924-2931. (in Chinese)
10	彭玉楼, 何怡刚, 林斌. 基于奇异值分解的压缩感知噪声信号重构算法[J]. 仪器仪表学报, 2012, 33(12): 2655-2660.
	PENG Y L, HE Y G, LIN B. Noise signal recovery algorithm based on singular value decomposition in compressed sensing[J]. Chinese Journal of Scientific Instrument, 2012, 33(12): 2655-2660. (in Chinese)
11	朱会杰, 王新晴, 芮挺, 等. 改进的匹配追踪在方波信号滤波中的应用[J]. 解放军理工大学学报(自然科学版), 2015, 16(4): 305-309.
	ZHU H J, WANG X Q, RUI T, et al. Implication of improved matching pursuit in de-noising for square wave[J]. Journal of PLA University of Science and Technology(Natural Science Edition), 2015, 16(4): 305-309. (in Chinese)
12	魏东, 周健鹏. K-SVD和OMP算法在超声信号去噪中的应用[J]. 应用声学, 2016, 35(2): 95-101.
	WEI D, ZHOU J P. Application of K-SVD and OMP algorithm on ultrasonic signal denoising[J]. Journal of Applied Acoustics, 2016, 35(2): 95-101. (in Chinese)
13	孔德阳, 彭华, 马金全. 基于人工鱼群算法的自适应随机共振方法研究[J]. 电子学报, 2017, 45(8): 1864-1872.
	KONG D Y, PENG H, MA J Q. A daptive stochastic resonance method based on artificial-fish swarm optimization[J]. Acta Electronica Sinica, 2017, 45(8): 1864-1872. (in Chinese)
14	张新明, 王霞, 康强, 等. GWO与ABC的混合优化算法及其聚类优化[J]. 电子学报, 2018, 46(10): 2430-2442.
	ZHANG X M, WANG X, KANG Q, et al. Hybrid grey wolf optimizer with artificial bee colony and its application to clustering optimization[J]. Acta Electronica Sinica, 2018, 46(10): 2430-2442. (in Chinese)
15	WANG D S, TAN D P, LIU L. Particle swarm optimization algorithm: An overview[J]. Soft Computing, 2018, 22(2): 387-408.
16	PAN W T. A new fruit fly optimization algorithm: Taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012, 26(5): 69-74.
17	石建平, 刘国平, 李培生, 等. 双策略协同进化果蝇优化算法及其应用[J/OL]. 计算机集成制造系统, (2020-11-26)[2021-04-20]. .
	SHI J P, LIU G P, LI P S, et al. Double strategies co-evolutionary fruit fly optimization algorithm and its application[J/OL]. Computer Integrated Manufacturing Systems, (2020-11-26)[2021-04-20]. (in Chinese)
18	凤丽洲, 王友卫, 韩琳琳, 等. 双重驱动的果蝇优化算法及其在PID控制器中的应用[J]. 控制与决策, 2021, 36(9): 2225-2233.
	FENG L Z, WANG Y W, HAN L L, et al. Double drive fruit fly optimization algorithm and its application in pid controller[J]. Control and Decision, 2021, 36(9): 2225-2233. (in Chinese)
19	王友卫, 凤丽洲. 基于双子群和分区采样的果蝇优化新算法[J]. 浙江大学学报(工学版), 2017, 51(11): 2292-2298.
	WANG Y W, FENG L Z. Novel double subgroups and partition sampling based fruit fly optimization algorithm[J]. Journal of Zhejiang University(Engineering Science), 2017, 51(11): 2292-2298. (in Chinese)
20	WU L, LIU Q, TIAN X, et al. A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems[J]. Knowledge-Based Systems, 2018, 144: 153-173.
21	ZHANG Y W, CUI G M, WU J T, et al. A novel multi-scale cooperative mutation fruit fly optimization algorithm[J]. Knowledge-Based Systems, 2016, 114: 24-35.
22	FU Y P, ZHOU M C, GUO X W, et al. Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm[J]. Journal of Cleaner Production, 2021, 278: 123364.
23	田旭, 李杰. 一种改进的果蝇优化算法及其在气动优化设计中的应用[J]. 航空学报, 2017, 38(4): 60-70.
	TIAN X, LI J. An improved fruit fly optimization algorithm and its application in aerodynamic optimization design[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(4): 60-70. (in Chinese)
24	HARIYANTO E, RAHIM R. Arnold's cat map algorithm in digital image encryption[J]. International Journal of Science and Research(IJSR), 2016, 5(10): 1363-1365.
25	鲍芳, 李军, 李旭. 基于高维广义猫映射的图像加密算法[J]. 西安理工大学学报, 2012, 28(2): 193-197.
	BAO F, LI J, LI X. Image encryption algorithm based on high dimensional chaotic cat maps[J]. Journal of Xi'an University of Technology, 2012, 28(2): 193-197. (in Chinese)
26	Qi A L, ZHANG G M, DONG M, et al. An artificial bee colony optimization based matching pursuit approach for ultrasonic echo estimation[J]. Ultrasonics, 2018, 88: 1-8.
27	任晓寰, 冯海泓, 杨震亚. 超声回波参数估计的初值选取方法[J]. 声学学报, 2020, 45(5): 728-738.
	REN X H, FENG H H, YANG Z Y. Selection of initial values for ultrasonic echo parameter estimation[J]. Acta Electronica Sinica, 2020, 45(5): 728-738. (in Chinese)

[1]	岳梦云, 白冰. 一种电机FOC算法DSP系统设计及实现[J]. 电子学报, 2020, 48(10): 2041-2046.
[2]	刘月峰, 赵光权, 彭喜元. 多核相关向量机优化模型的锂电池剩余寿命预测方法[J]. 电子学报, 2019, 47(6): 1285-1292.
[3]	刘项洋, 许勇, 郑孝遥, 陈付龙. 矢量基二维DCT修剪在DSP上的内存存取减少方法[J]. 电子学报, 2019, 47(3): 757-763.
[4]	吴林煌, 苏凯雄, 王琳, 陈志峰, 陈平平. 正交匹配追踪和BIC准则的自适应双频段预失真模型优化算法[J]. 电子学报, 2018, 46(9): 2149-2156.
[5]	孔德阳, 彭华, 马金全. 基于人工鱼群算法的自适应随机共振方法研究[J]. 电子学报, 2017, 45(8): 1864-1872.
[6]	鲁荣波, 陈留洋, 丁雷, 李建锋, 曾琳玲. 基于Contourlet域虚拟树结构和FOA-SVR的自适应鲁棒数字水印算法[J]. 电子学报, 2017, 45(3): 674-679.
[7]	刘项洋, 许勇. 快速DCT修剪在DSP上的内存访问优化方法[J]. 电子学报, 2016, 44(1): 227-232.
[8]	练秋生;张伟. 基于图像块分类稀疏表示的超分辨率重构算法[J]. 电子学报, 2012, 40(5): 920-925.
[9]	郭海燕;杨震;朱卫平. 一种新的基于稀疏分解的单通道混合语音分离方法[J]. 电子学报, 2012, 40(4): 762-768.
[10]	沈钲;孙义和. 一种支持同时多线程的VLIW DSP架构[J]. 电子学报, 2010, 38(2): 352-358.
[11]	王沁;李占才;齐悦. 基于两层流水线结构的FIR滤波器设计[J]. 电子学报, 2005, 33(2): 367-369.
[12]	张鹏;彭翔;牛憨笨. 一种虚拟光学数据加密的系统实现[J]. 电子学报, 2004, 32(10): 1585-1588.
[13]	卢结成;丁丁;丁晓兵;朱少华. 嵌入式Flash CISC/DSP微处理器的研究与实现[J]. 电子学报, 2003, 31(8): 1252-1254.
[14]	孙美香;陈泉林;吕国伟;李天俐. 基于Internet/Intranet的远程虚拟仪器[J]. 电子学报, 2002, 30(9): 1400-1401.
[15]	汪彤;张文俊;葛仁伟. 一种适用于任意色散媒质的新型FDTD方法研究[J]. 电子学报, 2001, 29(6): 788-791.

基于改进果蝇优化算法正交匹配追踪的超声信号降噪方法

An Improved Fruit Fly Optimization Algorithm Based Orthogonal Matching Pursuit for Ultrasonic Noise Reduction

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 27

相关文章 15

编辑推荐

Metrics