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基于稀疏分解的自适应数字锁定放大器微弱信号检测方法 被引量:2

A Weak Signal Detection Method of Adaptive Digital Lock-in Amplifier Based on Sparse Decomposition
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摘要 提出了一种利用软件算法来自适应地实现数字锁定放大器的微弱正弦信号的检测,即利用稀疏分解的匹配追踪算法来实现这种检测方法.将稀疏分解中原子库的原子(即具有随机性的频率和初始相位的正弦波)作为参考信号,利用匹配追踪算法来搜索原子库(由多个原子组成)中的最优原子——即与输入有用信号最匹配相干的原子并可以得到该最优原子的幅度,由该最优原子的幅度、频率和初始相位信息就可以估计得到输入有用信号的对应参数,从而检测估计出输入信号中的有用正弦信号.该方法无需人工调节参考信号的幅度、频率和初始相位,只需修改算法中的某些参数即可实现微弱信号检测,在应用中就会更加方便、灵活.计算机模拟表明,可以精确地检测出带噪信号中有用的正弦信号和调幅AM信号,并且在-40 dB以下极低信噪比环境下还可以同时检测出多个被强噪声淹没的有用正弦信号. The paper proposes a method which realizes adaptively weak sinusoidal signal detection of digital lock-in amplifier by software algorithm.The detection method is realized by matching pursuit(MP) algorithm of sparse decomposition.In the method,an atom of atoms of MP algorithm is a reference signal,and the atom is a sinusoidal wave which possesses random frequency and the initial phase,then it uses MP algorithm to search the optimal atom of atoms and obtains its amplitude,that is to say,the optimal atom is the atom of most matching correlation with a useful input signal,so it can estimate the corresponding parameters of the useful input signal through the messages of amplitude,frequency and the initial phase of the optimal atom and detect useful sinusoidal signal of the input one.With this method,it is not necessary to manually adjust the amplitude,frequency and the initial phase of the reference signal,and what is needed is only to modify some parameters of the algorithm to realize detection of weak signals.It is more convenient and flexible in the application.Computer simulation showed that this method can accurately detect useful sinusoidal signals and AM signals of the noise signals and it can detect multiple useful sinusoidal signals in a strong noise environment of the very low SNR of-40 dB.
出处 《西南大学学报(自然科学版)》 CAS CSCD 北大核心 2010年第1期160-168,共9页 Journal of Southwest University(Natural Science Edition)
基金 国家自然科学基金-中物院NSAF联合基金项目(10776040) 国家自然科学基金项目(60602057) 信号与信息处理重庆市重点实验室建设项目(CSTC 2009CA2003) 重庆市科委自然科学基金项目(CSTC 2006BB2373) 重庆市教委自然科学基金项目(KJ060509 KJ080517) 重庆邮电大学自然科学基金项目(A2006-04 A2006-86)的资助
关键词 微弱信号检测 锁定放大器 稀疏分解 匹配追踪 weak signal detection lock-in amplifier sparse decomposition matching pursuit
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参考文献5

  • 1Mallat S, Zhang Z. Matching Pursuit wilh Time Frequency Dictionaries [J]. IEEE Trans. on Signal Processing, 1993, 41(12): 3397--3415.
  • 2邹红星,周小波,李衍达.时频分析:回溯与前瞻[J].电子学报,2000,28(9):78-84. 被引量:135
  • 3Chen S, Donoho D. Saunders M. Atomic Decomposition by Basis Pursuit[J].SlAM Journal on Scientific Computing, 1999, 20: 33-61.
  • 4Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal Matching Pursuit= Recursive Function Approximation with Applications to Wavelet Decomposition [C]. In 27th Asilomar Conf. on Signals, Systems and Comput. November, 1993.
  • 5Donoho DL, Elad M. Optimally Sparse Representation in General (Non Orthogonal) Dictionaries Via l1 Minimization [J].Proc Natl Acad Sci, 2002, 100:2197-2202.

二级参考文献2

共引文献134

同被引文献13

  • 1尹明,尹忠科,王建英.利用蚁群算法实现基于MP的信号稀疏分解[J].计算机工程与应用,2006,42(36):47-48. 被引量:4
  • 2高晋占.微弱信号检测[M]北京:清华大学出版社,2004154-176.
  • 3Mallat S,Zhang Z. Matching Pursuit with time-fre-quency dictionaries[J].IEEE Trans On Signal Pro-cessing,1993,(12):3397-3415.
  • 4Liu Tao. Study on casting ultrasonic signal extraction algorithm based on improved MP[A].Wuhan:IEEE,2010.115-118.
  • 5王建英;尹忠科;张春梅.信号与图像的稀疏分解及初步应用[M]成都:西南交通大学出版社,200666-67.
  • 6MALLAT S G,ZHANG Z. Matching pursuit with timefrequencydictionaries[J]. IEEE Transactions on SignalProcessing,1993,41(12):3397-3415.
  • 7CHEN S,DONOHO D,SAUNDERS M. Atomic Decompo-sition by Basis Pursuit[J]. SIAM Journal on ScientificComputing,1999,20(1):33-61.
  • 8LIU T. Study on casting ultrasonic signal extraction al-gorithm based on improved MP[C] / / Proceedings ofFuture Computer and Communication(ICFCC-2010).Wuhan:IEEE,2010:115-118.
  • 9李月,徐凯,杨宝俊,袁野,吴宁.混沌振子系统周期解几何特征量分析与微弱周期信号的定量检测[J].物理学报,2008,57(6):3353-3358. 被引量:26
  • 10邓承志,汪胜前,曹汉强.基于多原子快速匹配追踪的图像编码算法[J].电子与信息学报,2009,31(8):1807-1811. 被引量:2

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