American Institute of Mathematical Sciences

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January  2013, 9(1): 205-225. doi: 10.3934/jimo.2013.9.205

Applications of a nonlinear optimization solver and two-stage comprehensive Denoising techniques for optimum underwater wideband sonar echolocation system

Chien Hsun Tseng 1,

1. 

Department of Information Engineering, Kun Shan University, Taiwan

Received  September 2011 Revised  June 2012 Published  December 2012

This paper focuses on empirical design and performs real data test of a novel algorithm that contributes to the purpose of solving a specific SIP problem arising from a classical wideband active sonar echo location system in noisy environment. The algorithm is achieved by firstly isolating potential contact signals of interest embedded in the scattered returns through the first-stage denoising using an adaptive noise canceling (ANC) neuro-fuzzy scheme. The ANC output is then feed into an iterative target motion analysis (TMA) scheme composed of the second-stage denoising and optimal motion estimation. In the first-stage denoising, the adaptive neuro-fuzzy inference system (ANFIS) is the core processor of ANC for tracking both the linear and nonlinear relations among complex contact signals. The second-stage denoising is appealed for further noise compression and is accomplished via trimmed-mean (TM) levelization and discrete wavelet denoising (WDeN). The two-stage comprehensive denoising techniques yield fine tuned signals for the system deconvolution based on solving a semi-infinite programming (SIP) problem. These two schemes form an ANC-TMA(CWT) algorithm for rapid processing of target echoes and provide a higher degree of signal detection capability with an increased robustness against false signal detections. Advantages and simulation results are discussed in terms of detection performance and computational time consumption.
Keywords: Wideband acoustical signal, reverberation, adaptive neuro-fuzzy inference system, wideband cross correlation, adaptive noise canceler, active sonar echolocation, optimal wavelet transforms..
Mathematics Subject Classification: 90C3.
Citation: Chien Hsun Tseng. Applications of a nonlinear optimization solver and two-stage comprehensive Denoising techniques for optimum underwater wideband sonar echolocation system. Journal of Industrial & Management Optimization, 2013, 9 (1) : 205-225. doi: 10.3934/jimo.2013.9.205
References:
[1]

W. S. Burdic, "Underwater Acoustic System Analysis,", Prentice Hall, (1991).   Google Scholar

[2]

P. C. Etter, "Underwater Acoustic Modeling: Principles, Techniques and Applications,", London: E&FN Spon, (1996).   Google Scholar

[3]

H. Van Trees, "Detection, Estimation, and Modulation Theory, Parts I, II, III,", Wiley, (1968).   Google Scholar

[4]

R. A. Altes, Target position estimation in radar and sonar, generalized ambiguity analysis for maximum likelihood parameter estimation,, Proc. IEEE, 67 (1979), 920.  doi: 10.1109/PROC.1979.11355.  Google Scholar

[5]

E. J. Kelly and R. P. Wishner, Matched-filter theory for high-velocity targets,, IEEE Trans. Military Elect., 9 (1965), 56.  doi: 10.1109/TME.1965.4323176.  Google Scholar

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A. Carlson, P. Crilly and J. Rutledge, "Communication Systems-An Introduction to Signals and Noise in Electrical Communication,", e/4, (2002).   Google Scholar

[7]

L. G. Weiss, Wavelets and wideband correlation processing,, IEEE Signal Processing Magazine, (1994), 13.  doi: 10.1109/79.252866.  Google Scholar

[8]

H. Sibul and G. Weiss, A wideband wavelet based estimator correlator and its properties,, Multidimensional Systems and Signal Processing, 13 (2002), 157.  doi: 10.1023/A:1014488726761.  Google Scholar

[9]

H. Naparst, Dense target signal processing,, IEEE Trans. Inform. Theory, 37 (1991), 317.  doi: 10.1109/18.75247.  Google Scholar

[10]

T. Kadota and D. Romain, Optimum detection of Gaussian signal fields in the multipath-anisotropic noise environment and numerical evaluation of detection probabilities,, IEEE Trans. Information Theory, 23 (1977), 164.   Google Scholar

[11]

P. Delaney and D. Walsh, Performance analysis of the incoherent and skewness matched filter detectors in multipath environments,, IEEE Journal of Oceanic Engineering, 20 (1995), 80.  doi: 10.1109/48.380243.  Google Scholar

[12]

C. H. Tseng and M. Cole, Adaptive neuro-fuzzy inference systems for wideband signal recovery in a noise-limited environment,, FUZZ-IEEE, 2007 (): 757.   Google Scholar

[13]

C. H. Tseng and M. Cole, Optimum multi-target detection using an ANC neuro-fuzzy scheme and wideband replica correlator,, IEEE ICASSP, 2009 (): 1369.   Google Scholar

[14]

B. Widrow et al., Adaptive noise cancelling: Principles and applications,, IEEE Proc., 63 (1975), 1692.   Google Scholar

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J-S R. Jang, C. T. Sun and E. Mizutani, "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence,", Pearson Education Taiwan Ltd., (2004).   Google Scholar

[16]

O. Kipersztok, Active control of broadband noise using fuzzy logic,, Proc. IEEE Int. Conf. Fuzzy Sys., II (1993), 906.   Google Scholar

[17]

O. Kipersztok and H. Ron, Fuzzy active control of a distributed broadband noise source,, Proc. IEEE Int. Conf. Fuzzy Sys., II (1994), 1342.  doi: 10.1109/FUZZY.1994.343617.  Google Scholar

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S. Haykin, "Adaptive Filter Theory,", e/4, (2001).   Google Scholar

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"Xilinx DSP (2005): Designing for Optimal Results: High-Performance Dsp Using Virtex-4 FPGAs,", DSP solution advanced design guide,, e/1, ().   Google Scholar

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R. William and D. Zipser, A learning algorithm for continually running fully recurrent neural networks,, Neural Computation, 1 (1989), 270.  doi: 10.1162/neco.1989.1.2.270.  Google Scholar

[21]

Q. Zhang and A. Benveniste, Wavelet networks,, IEEE, 3 (1992), 889.  doi: 10.1109/72.165591.  Google Scholar

[22]

D. L. Donoho, De-Noising by soft-thresholding,, IEEE Trans. on Inf. Theory, 41 (1995), 613.  doi: 10.1109/18.382009.  Google Scholar

[23]

P. P. Gandhi and S. A. Kassam, Analysis of CFAR processors in nonhomogenous background,, IEEE Trans. Aerosp. Electron. Syst., 24 (1988), 427.  doi: 10.1109/7.7185.  Google Scholar

[24]

D. A. Abraham and P. K. Willett, Active sonar detection in shallow water using the page test,, IEEE Oceanic Eng., 27 (2002), 35.  doi: 10.1109/48.989883.  Google Scholar

[25]

T. G. Manickam, R. J. Vaccaro and D. W. Tufts, A least-squares algorithm for multipath time-delay estimation,, IEEE Trans. Signal Processing, 42 (1994), 3229.  doi: 10.1109/78.330381.  Google Scholar

[26]

M. A. Mansour, B. V. Smith and J. A. Edwards, PC-based real-time active sonar simulator,, IEE Proc.-Radar, 144 (1997), 227.  doi: 10.1049/ip-rsn:19971260.  Google Scholar

[27]

S. Stein, Algorithm for ambiguity function processing,, IEEE Trans. Acoust. Speech Signal Proc., 29 (1981), 588.  doi: 10.1109/TASSP.1981.1163621.  Google Scholar

[28]

L. Auslander and I. Gertner, Wideband ambiguity function generation and $ax+b$ group,, from Signal Processing, 1 (1990), 1.   Google Scholar

[29]

D. Alexandrou and C. D. Moustier, Adaptive noise canceling applied to sea beam sidelobe interference rejection,, IEEE J. Oceanic Eng., 13 (1988), 70.  doi: 10.1109/48.556.  Google Scholar

[30]

O. Rioul and M. Vetterli, Wavelets and signal processing,, IEEE Signal Process. Magazine, (1991), 14.  doi: 10.1109/79.91217.  Google Scholar

[31]

S. Mallat, "A Wavelet Tour of Signal Processing,", 2nd edition, (1999).   Google Scholar

[32]

R. Young, "Wavelet Theory and Its Applications,", Kluwer Academic Publisher, (1993).   Google Scholar

[33]

R. P. Brent, "Algorithms for Minimization without Derivatives,", Prentice-Hall, (1973).   Google Scholar

[34]

D. Alexandrou, Signal recovery in a reverberation-limited environment,, IEEE Journal of Oceanic Engineering, 12 (1987), 553.  doi: 10.1109/JOE.1987.1145285.  Google Scholar

[35]

J. Sadowsky, Investigation of signal characteristics using the continuous wavelet transform,, Johns Hopkins APL Technical Digest, 17 (1996), 258.   Google Scholar

[36]

M. Aineto and S. Lawson, Narrowband signal detection in a reverberation-limited environment,, OCEAN'97. MTS/IEEE Proc., 1 (1997), 27.   Google Scholar

[37]

M. Sugeno, "Industrial Applications of Fuzzy Control,", Elsevier Science Pub. Co., (1985).   Google Scholar

show all references

References:
[1]

W. S. Burdic, "Underwater Acoustic System Analysis,", Prentice Hall, (1991).   Google Scholar

[2]

P. C. Etter, "Underwater Acoustic Modeling: Principles, Techniques and Applications,", London: E&FN Spon, (1996).   Google Scholar

[3]

H. Van Trees, "Detection, Estimation, and Modulation Theory, Parts I, II, III,", Wiley, (1968).   Google Scholar

[4]

R. A. Altes, Target position estimation in radar and sonar, generalized ambiguity analysis for maximum likelihood parameter estimation,, Proc. IEEE, 67 (1979), 920.  doi: 10.1109/PROC.1979.11355.  Google Scholar

[5]

E. J. Kelly and R. P. Wishner, Matched-filter theory for high-velocity targets,, IEEE Trans. Military Elect., 9 (1965), 56.  doi: 10.1109/TME.1965.4323176.  Google Scholar

[6]

A. Carlson, P. Crilly and J. Rutledge, "Communication Systems-An Introduction to Signals and Noise in Electrical Communication,", e/4, (2002).   Google Scholar

[7]

L. G. Weiss, Wavelets and wideband correlation processing,, IEEE Signal Processing Magazine, (1994), 13.  doi: 10.1109/79.252866.  Google Scholar

[8]

H. Sibul and G. Weiss, A wideband wavelet based estimator correlator and its properties,, Multidimensional Systems and Signal Processing, 13 (2002), 157.  doi: 10.1023/A:1014488726761.  Google Scholar

[9]

H. Naparst, Dense target signal processing,, IEEE Trans. Inform. Theory, 37 (1991), 317.  doi: 10.1109/18.75247.  Google Scholar

[10]

T. Kadota and D. Romain, Optimum detection of Gaussian signal fields in the multipath-anisotropic noise environment and numerical evaluation of detection probabilities,, IEEE Trans. Information Theory, 23 (1977), 164.   Google Scholar

[11]

P. Delaney and D. Walsh, Performance analysis of the incoherent and skewness matched filter detectors in multipath environments,, IEEE Journal of Oceanic Engineering, 20 (1995), 80.  doi: 10.1109/48.380243.  Google Scholar

[12]

C. H. Tseng and M. Cole, Adaptive neuro-fuzzy inference systems for wideband signal recovery in a noise-limited environment,, FUZZ-IEEE, 2007 (): 757.   Google Scholar

[13]

C. H. Tseng and M. Cole, Optimum multi-target detection using an ANC neuro-fuzzy scheme and wideband replica correlator,, IEEE ICASSP, 2009 (): 1369.   Google Scholar

[14]

B. Widrow et al., Adaptive noise cancelling: Principles and applications,, IEEE Proc., 63 (1975), 1692.   Google Scholar

[15]

J-S R. Jang, C. T. Sun and E. Mizutani, "Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence,", Pearson Education Taiwan Ltd., (2004).   Google Scholar

[16]

O. Kipersztok, Active control of broadband noise using fuzzy logic,, Proc. IEEE Int. Conf. Fuzzy Sys., II (1993), 906.   Google Scholar

[17]

O. Kipersztok and H. Ron, Fuzzy active control of a distributed broadband noise source,, Proc. IEEE Int. Conf. Fuzzy Sys., II (1994), 1342.  doi: 10.1109/FUZZY.1994.343617.  Google Scholar

[18]

S. Haykin, "Adaptive Filter Theory,", e/4, (2001).   Google Scholar

[19]

"Xilinx DSP (2005): Designing for Optimal Results: High-Performance Dsp Using Virtex-4 FPGAs,", DSP solution advanced design guide,, e/1, ().   Google Scholar

[20]

R. William and D. Zipser, A learning algorithm for continually running fully recurrent neural networks,, Neural Computation, 1 (1989), 270.  doi: 10.1162/neco.1989.1.2.270.  Google Scholar

[21]

Q. Zhang and A. Benveniste, Wavelet networks,, IEEE, 3 (1992), 889.  doi: 10.1109/72.165591.  Google Scholar

[22]

D. L. Donoho, De-Noising by soft-thresholding,, IEEE Trans. on Inf. Theory, 41 (1995), 613.  doi: 10.1109/18.382009.  Google Scholar

[23]

P. P. Gandhi and S. A. Kassam, Analysis of CFAR processors in nonhomogenous background,, IEEE Trans. Aerosp. Electron. Syst., 24 (1988), 427.  doi: 10.1109/7.7185.  Google Scholar

[24]

D. A. Abraham and P. K. Willett, Active sonar detection in shallow water using the page test,, IEEE Oceanic Eng., 27 (2002), 35.  doi: 10.1109/48.989883.  Google Scholar

[25]

T. G. Manickam, R. J. Vaccaro and D. W. Tufts, A least-squares algorithm for multipath time-delay estimation,, IEEE Trans. Signal Processing, 42 (1994), 3229.  doi: 10.1109/78.330381.  Google Scholar

[26]

M. A. Mansour, B. V. Smith and J. A. Edwards, PC-based real-time active sonar simulator,, IEE Proc.-Radar, 144 (1997), 227.  doi: 10.1049/ip-rsn:19971260.  Google Scholar

[27]

S. Stein, Algorithm for ambiguity function processing,, IEEE Trans. Acoust. Speech Signal Proc., 29 (1981), 588.  doi: 10.1109/TASSP.1981.1163621.  Google Scholar

[28]

L. Auslander and I. Gertner, Wideband ambiguity function generation and $ax+b$ group,, from Signal Processing, 1 (1990), 1.   Google Scholar

[29]

D. Alexandrou and C. D. Moustier, Adaptive noise canceling applied to sea beam sidelobe interference rejection,, IEEE J. Oceanic Eng., 13 (1988), 70.  doi: 10.1109/48.556.  Google Scholar

[30]

O. Rioul and M. Vetterli, Wavelets and signal processing,, IEEE Signal Process. Magazine, (1991), 14.  doi: 10.1109/79.91217.  Google Scholar

[31]

S. Mallat, "A Wavelet Tour of Signal Processing,", 2nd edition, (1999).   Google Scholar

[32]

R. Young, "Wavelet Theory and Its Applications,", Kluwer Academic Publisher, (1993).   Google Scholar

[33]

R. P. Brent, "Algorithms for Minimization without Derivatives,", Prentice-Hall, (1973).   Google Scholar

[34]

D. Alexandrou, Signal recovery in a reverberation-limited environment,, IEEE Journal of Oceanic Engineering, 12 (1987), 553.  doi: 10.1109/JOE.1987.1145285.  Google Scholar

[35]

J. Sadowsky, Investigation of signal characteristics using the continuous wavelet transform,, Johns Hopkins APL Technical Digest, 17 (1996), 258.   Google Scholar

[36]

M. Aineto and S. Lawson, Narrowband signal detection in a reverberation-limited environment,, OCEAN'97. MTS/IEEE Proc., 1 (1997), 27.   Google Scholar

[37]

M. Sugeno, "Industrial Applications of Fuzzy Control,", Elsevier Science Pub. Co., (1985).   Google Scholar

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