基于改进FOA匹配追踪的超声信号处理研究
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摘要
针对信号稀疏分解中常用的匹配追踪算法运算耗时较长、分解不够准确等问题,将结合混沌映射与局部搜索思想的改进果蝇优化算法应用于匹配追踪中,以提高信号稀疏分解的速度与准确度。将该算法应用于超声回波仿真信号和换热管污垢超声检测实验信号的处理,并与其他几种常用匹配追踪算法进行比较。结果表明该改进果蝇优化匹配追踪算法,在提高信号稀疏分解速度的同时,获得了较好的降噪与信息提取效果,对超声检测信号的处理具有较重要意义。
Aiming at the problems of common matching pursuit algorithms in signal sparse decomposition,such as time-consuming calculation and inaccurate decomposition,this article proposes a new improved fruit fly optimization algorithm(FOA),which combines the chaotic mapping and local searching,is applied in matching pursuit to improve the speed and accuracy of the signal sparse decomposition.The new method was applied both in the processing of ultrasonic echo simulation signal and the heat exchanger fouling ultrasonic detection signal,and compared with several other common matching pursuit algorithms.The results show that the improved fruit fly optimization matching pursuit algorithm can increase the speed of the signal sparse decomposition and obtain better de-noising and information extracting effect at the same time.This method is of great importance to the processing of ultrasonic detection signals.
Aiming at the problems of common matching pursuit algorithms in signal sparse decomposition,such as time-consuming calculation and inaccurate decomposition,this article proposes a new improved fruit fly optimization algorithm(FOA),which combines the chaotic mapping and local searching,is applied in matching pursuit to improve the speed and accuracy of the signal sparse decomposition.The new method was applied both in the processing of ultrasonic echo simulation signal and the heat exchanger fouling ultrasonic detection signal,and compared with several other common matching pursuit algorithms.The results show that the improved fruit fly optimization matching pursuit algorithm can increase the speed of the signal sparse decomposition and obtain better de-noising and information extracting effect at the same time.This method is of great importance to the processing of ultrasonic detection signals.
引文
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[15]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 onhigh dimensional chaotic cat maps[J].Journal of Xi’anUniversity of Technology,2012,28(2):193-197.
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[18]高卫风,刘三阳.一种高效粒子群优化算法[J].控制与决策,2011,26(8):1158-1162.GAO W F,LIU S Y.An efficient particle swarm optimiza-tion[J].Control and Decision,2011,26(8):1158-1162.
[19]孙灵芳,杨善让,徐志明,等.一种新型在线冷却水动态模拟试验装置[J].仪器仪表学报,2002,23(增刊3):630-631.SUN L F,YANG SH R,XU ZH M,et al.A new on-line cool-ing water simulated dynamic test equipment[J].ChineseJournal of Scientific Instrument,2002,23(Suppl.3):630-631.
[2]OZDEN H O,PURI V M.Computational analysis of foul-ing by low energy surfaces[J].Journal of Food Engineer-ing,2010,99:250-256.
[3]张莹,王耀南.基于局部加权偏最小二乘法的冷凝器污垢预测[J].仪器仪表学报,2010,31(2):299-304.ZHANG Y,WANG Y N.Prediction of condenser foulingbased on locally weighted partial least squares regressionalgorithm[J].Chinese Journal of Scientific Instrument,2010,31(2):299-304.
[4]SUN L F,FENG G L,LU W X.Fouling detection based on analysis of ultrasonic time-domain reflectometry usingwavelet transform[J].Communications in Computer andInformation Science,2011,153:347-352.
[5]CANDES E J,TAO T.Near-optimal signal recovery fromrandom projection:universal encoding strategies[J].IEEE Trans Inform Theory,2006,52:5406-5426.
[6]MALLAT S G,ZHANG ZH F.Matching pursuits withtime-frequency dictionaries[J].IEEE Transaction onSignal Processing,1993,41(2):3397-3414.
[7]闵锐,杨倩倩,皮亦鸣,等.基于正则化正交匹配追踪的SAR层析成像[J].电子测量与仪器学报,2012,26(12):1069-1073.MIN R,YANG Q Q,PI Y M,et al.SAR tomography imagingbased on regularized orthogonal matching pursuit[J].Journalof Electronic Measurement and Instrument,2012,26(12):1069-1073.
[8]肖正安.语音信号稀疏分解的FOA实现[J].计算机工程与应用,2013,49(10):232-234.XIAO ZH AN.Speech signal sparse decomposition withFOA[J].Computer Engineering and Applications,2013,49(10):232-234.
[9]蔡涵鹏,贺振华,高刚,等.基于混合优化算法的地震数据匹配追踪分解[J].中南大学学报:自然科学版,2013,44(2):687-694.CAI H P,HE ZH H,GAO G,et al.Seismic data matchingpursuit using hybrid optimization algorithm and its appli-cation[J].Journal of Central South University:Scienceand Technology,2013,44(2):687-694.
[10]齐爱玲,马宏伟,刘涛.基于改进人工鱼群优化算法的超声信号稀疏分解[J].仪器仪表学报,2009,30(12):2480-2484.QI AI L,MA H W,LIU T.Ultrasonic signal sparse de-composition based on improved artificial fish swarm opti-mization algorithm[J].Chinese Journal of Scientific In-strument,2009,30(12):2480-2484.
[11]PAN W T.A new fruit fly optimization algorithm:takingthe financial distress model as an example[J].Knowl-edge-Based Systems,2012,26:69-74.
[12]程慧,刘成忠.基于混沌映射的混合果蝇优化算法[J].计算机工程,2013,39(5):218-221.CHENG H,LIU CH ZH.Mixed fruit fly optimization algo-rithm based on chaotic mapping[J].Computer Engineer-ing,2013,39(5):218-221.
[13]ARNOLD E A,AVEZ A.Ergodic problems of classicalmechanics[M].New Jersey:Benjamin,W A,1968.
[14]王芳,戴永寿,王少水.改进的混沌遗传算法[J].计算机工程与应用,2010,46(6):29-32.WANG F,DAI Y SH,WANG SH SH.Modified chaos-ge-netic algorithm[J].Computer Engineering and Applica-tions,2010,46(6):29-32.
[15]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 onhigh dimensional chaotic cat maps[J].Journal of Xi’anUniversity of Technology,2012,28(2):193-197.
[16]HZMZACEBI C,KUTAY F.Continuous functions minimi-zation by dynamic random search technique[J].AppliedMathematical Modelling,2007,31(10):2189-2198.
[17]HAMZACEBI C.Improving genetic algorithms performanceby local search for continuous function optimization[J].Ap-plied Mathematical Modelling,2008,196(1):309-317.
[18]高卫风,刘三阳.一种高效粒子群优化算法[J].控制与决策,2011,26(8):1158-1162.GAO W F,LIU S Y.An efficient particle swarm optimiza-tion[J].Control and Decision,2011,26(8):1158-1162.
[19]孙灵芳,杨善让,徐志明,等.一种新型在线冷却水动态模拟试验装置[J].仪器仪表学报,2002,23(增刊3):630-631.SUN L F,YANG SH R,XU ZH M,et al.A new on-line cool-ing water simulated dynamic test equipment[J].ChineseJournal of Scientific Instrument,2002,23(Suppl.3):630-631.
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