Ultrasonic signal compressive detection with sub-Nyquist sampling rate

This study presents a compressed sensing (CS) based sampling approach for repetitive ultrasonic signal. Proposed system considers recovering ultrasonic signal with high equivalent sampling frequency from samples captured using analog-to-digital converter (ADC) clocked at a rate lower than Nyquist rate. A basis function is constructed to realize ultrasonic signal sparse representation, which paves the way for applying CS theory to ultrasonic signal sub-Nyquist sampling. A sampling architecture, applicable for ultrasonic compressive detection, is developed. With this sampling system, an ultrasonic signal sampled at ultralow rate, but still can be recovered with overwhelming probability.

[1]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[2]  Michael B. Wakin,et al.  An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition] , 2008 .

[3]  James R. Andrews,et al.  Random Sampling Oscilloscope for the Observation of Mercury Switch Closure Transition Times , 1973 .

[4]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[5]  Greg Humphreys,et al.  Sampling and Reconstruction , 2010 .

[6]  Justin K. Romberg,et al.  Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals , 2009, IEEE Transactions on Information Theory.

[7]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[8]  Barrie Hayes-Gill,et al.  High-Speed Digitizing of Repetitive Waveforms Using Accurate Interleaved Sampling , 2007, IEEE Transactions on Instrumentation and Measurement.

[9]  J. Tropp.  Algorithms for simultaneous sparse approximation. Part II: Convex relaxation , 2006, Signal Process..

[10]  J. CandesE.,et al.  Robust uncertainty principles , 2006 .

[11]  Peter Graham Fish,et al.  Physics and Instrumentation of Diagnostic Medical Ultrasound , 1990 .

[12]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[13]  N. S. Nahman,et al.  Random Sampling Oscillography , 1964 .

[14]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[15]  Sampling and reconstruction of bandpass signal , 1996, Proceedings of Third International Conference on Signal Processing (ICSP'96).

[16]  Yong Ching Lim,et al.  Time-Interleaved Analog-to-Digital-Converter Compensation Using Multichannel Filters , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[17]  Yonina C. Eldar,et al.  Multichannel Sampling of Pulse Streams at the Rate of Innovation , 2010, IEEE Transactions on Signal Processing.

[18]  Hannu Olkkonen,et al.  Sampling and Reconstruction of Transient Signals by Parallel Exponential Filters , 2010, IEEE Transactions on Circuits and Systems II: Express Briefs.

[19]  S. Kirolos,et al.  Random Sampling for Analog-to-Information Conversion of Wideband Signals , 2006, 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software.

[20]  M. Vetterli,et al.  Sparse Sampling of Signal Innovations , 2008, IEEE Signal Processing Magazine.

[21]  Fernand S. Cohen.  Modeling of ultrasound speckle with application in flaw detection in metals , 1992, IEEE Trans. Signal Process..

[22]  Yonina C. Eldar,et al.  Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals , 2007, IEEE Transactions on Signal Processing.