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

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.
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 and 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, 2nd Edition, 1991.

[2]

P. C. Etter, "Underwater Acoustic Modeling: Principles, Techniques and Applications," London: E&FN Spon, 2nd e/d, 1996.

[3]

H. Van Trees, "Detection, Estimation, and Modulation Theory, Parts I, II, III," Wiley, New York, 1968.

[4]

R. A. Altes, Target position estimation in radar and sonar, generalized ambiguity analysis for maximum likelihood parameter estimation, Proc. IEEE, 67 (1979), 920-930. doi: 10.1109/PROC.1979.11355.

[5]

E. J. Kelly and R. P. Wishner, Matched-filter theory for high-velocity targets, IEEE Trans. Military Elect., 9 (1965), 56-69. doi: 10.1109/TME.1965.4323176.

[6]

A. Carlson, P. Crilly and J. Rutledge, "Communication Systems-An Introduction to Signals and Noise in Electrical Communication," e/4, McGraw Hill, Taipei, 2002.

[7]

L. G. Weiss, Wavelets and wideband correlation processing, IEEE Signal Processing Magazine, (1994), 13-32. doi: 10.1109/79.252866.

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H. Sibul and G. Weiss, A wideband wavelet based estimator correlator and its properties, Multidimensional Systems and Signal Processing, 13 (2002), 157-186. doi: 10.1023/A:1014488726761.

[9]

H. Naparst, Dense target signal processing, IEEE Trans. Inform. Theory, 37 (1991), 317-327. doi: 10.1109/18.75247.

[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), 164-178.

[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), 80-84. doi: 10.1109/48.380243.

[12]

C. H. Tseng and M. Cole, Adaptive neuro-fuzzy inference systems for wideband signal recovery in a noise-limited environment, FUZZ-IEEE, 2007, 757-762.

[13]

C. H. Tseng and M. Cole, Optimum multi-target detection using an ANC neuro-fuzzy scheme and wideband replica correlator, IEEE ICASSP, 2009, 1369-1372, Taipei, Taiwan.

[14]

B. Widrow et al., Adaptive noise cancelling: Principles and applications, IEEE Proc., 63 (1975), 1692-1716.

[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.

[16]

O. Kipersztok, Active control of broadband noise using fuzzy logic, Proc. IEEE Int. Conf. Fuzzy Sys., II (1993), 906-911.

[17]

O. Kipersztok and H. Ron, Fuzzy active control of a distributed broadband noise source, Proc. IEEE Int. Conf. Fuzzy Sys., II (1994), 1342-1347. doi: 10.1109/FUZZY.1994.343617.

[18]

S. Haykin, "Adaptive Filter Theory," e/4, Prentice-Hall, London. 2001.

[19]

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

[20]

R. William and D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1 (1989), 270-280. doi: 10.1162/neco.1989.1.2.270.

[21]

Q. Zhang and A. Benveniste, Wavelet networks, IEEE, Trans. Neural Networks, 3 (1992), 889-898. doi: 10.1109/72.165591.

[22]

D. L. Donoho, De-Noising by soft-thresholding, IEEE Trans. on Inf. Theory, 41 (1995), 613-627. doi: 10.1109/18.382009.

[23]

P. P. Gandhi and S. A. Kassam, Analysis of CFAR processors in nonhomogenous background, IEEE Trans. Aerosp. Electron. Syst., 24 (1988), 427-445. doi: 10.1109/7.7185.

[24]

D. A. Abraham and P. K. Willett, Active sonar detection in shallow water using the page test, IEEE Oceanic Eng., 27 (2002), 35-46. doi: 10.1109/48.989883.

[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), 3229-3233. doi: 10.1109/78.330381.

[26]

M. A. Mansour, B. V. Smith and J. A. Edwards, PC-based real-time active sonar simulator, IEE Proc.-Radar, Sonar Navig., 144 (1997), 227-233. doi: 10.1049/ip-rsn:19971260.

[27]

S. Stein, Algorithm for ambiguity function processing, IEEE Trans. Acoust. Speech Signal Proc., 29 (1981), 588-599. doi: 10.1109/TASSP.1981.1163621.

[28]

L. Auslander and I. Gertner, Wideband ambiguity function generation and $ax+b$ group, from Signal Processing, Part I: Signal Processing, Springer-Verlag, New York, 1 (1990), 1-12.

[29]

D. Alexandrou and C. D. Moustier, Adaptive noise canceling applied to sea beam sidelobe interference rejection, IEEE J. Oceanic Eng., 13 (1988), 70-76. doi: 10.1109/48.556.

[30]

O. Rioul and M. Vetterli, Wavelets and signal processing, IEEE Signal Process. Magazine, (1991), 14-38. doi: 10.1109/79.91217.

[31]

S. Mallat, "A Wavelet Tour of Signal Processing," 2nd edition, Academic Press, Uk. 1999.

[32]

R. Young, "Wavelet Theory and Its Applications," Kluwer Academic Publisher, Bosten. 1993.

[33]

R. P. Brent, "Algorithms for Minimization without Derivatives," Prentice-Hall, Englewood Cliffs, New Jersey. 1973.

[34]

D. Alexandrou, Signal recovery in a reverberation-limited environment, IEEE Journal of Oceanic Engineering, 12 (1987), 553-559. doi: 10.1109/JOE.1987.1145285.

[35]

J. Sadowsky, Investigation of signal characteristics using the continuous wavelet transform, Johns Hopkins APL Technical Digest, 17 (1996), 258-269.

[36]

M. Aineto and S. Lawson, Narrowband signal detection in a reverberation-limited environment, OCEAN'97. MTS/IEEE Proc., 1 (1997), 27-32.

[37]

M. Sugeno, "Industrial Applications of Fuzzy Control," Elsevier Science Pub. Co., 1985.

show all references

References:
[1]

W. S. Burdic, "Underwater Acoustic System Analysis," Prentice Hall, 2nd Edition, 1991.

[2]

P. C. Etter, "Underwater Acoustic Modeling: Principles, Techniques and Applications," London: E&FN Spon, 2nd e/d, 1996.

[3]

H. Van Trees, "Detection, Estimation, and Modulation Theory, Parts I, II, III," Wiley, New York, 1968.

[4]

R. A. Altes, Target position estimation in radar and sonar, generalized ambiguity analysis for maximum likelihood parameter estimation, Proc. IEEE, 67 (1979), 920-930. doi: 10.1109/PROC.1979.11355.

[5]

E. J. Kelly and R. P. Wishner, Matched-filter theory for high-velocity targets, IEEE Trans. Military Elect., 9 (1965), 56-69. doi: 10.1109/TME.1965.4323176.

[6]

A. Carlson, P. Crilly and J. Rutledge, "Communication Systems-An Introduction to Signals and Noise in Electrical Communication," e/4, McGraw Hill, Taipei, 2002.

[7]

L. G. Weiss, Wavelets and wideband correlation processing, IEEE Signal Processing Magazine, (1994), 13-32. doi: 10.1109/79.252866.

[8]

H. Sibul and G. Weiss, A wideband wavelet based estimator correlator and its properties, Multidimensional Systems and Signal Processing, 13 (2002), 157-186. doi: 10.1023/A:1014488726761.

[9]

H. Naparst, Dense target signal processing, IEEE Trans. Inform. Theory, 37 (1991), 317-327. doi: 10.1109/18.75247.

[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), 164-178.

[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), 80-84. doi: 10.1109/48.380243.

[12]

C. H. Tseng and M. Cole, Adaptive neuro-fuzzy inference systems for wideband signal recovery in a noise-limited environment, FUZZ-IEEE, 2007, 757-762.

[13]

C. H. Tseng and M. Cole, Optimum multi-target detection using an ANC neuro-fuzzy scheme and wideband replica correlator, IEEE ICASSP, 2009, 1369-1372, Taipei, Taiwan.

[14]

B. Widrow et al., Adaptive noise cancelling: Principles and applications, IEEE Proc., 63 (1975), 1692-1716.

[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.

[16]

O. Kipersztok, Active control of broadband noise using fuzzy logic, Proc. IEEE Int. Conf. Fuzzy Sys., II (1993), 906-911.

[17]

O. Kipersztok and H. Ron, Fuzzy active control of a distributed broadband noise source, Proc. IEEE Int. Conf. Fuzzy Sys., II (1994), 1342-1347. doi: 10.1109/FUZZY.1994.343617.

[18]

S. Haykin, "Adaptive Filter Theory," e/4, Prentice-Hall, London. 2001.

[19]

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

[20]

R. William and D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1 (1989), 270-280. doi: 10.1162/neco.1989.1.2.270.

[21]

Q. Zhang and A. Benveniste, Wavelet networks, IEEE, Trans. Neural Networks, 3 (1992), 889-898. doi: 10.1109/72.165591.

[22]

D. L. Donoho, De-Noising by soft-thresholding, IEEE Trans. on Inf. Theory, 41 (1995), 613-627. doi: 10.1109/18.382009.

[23]

P. P. Gandhi and S. A. Kassam, Analysis of CFAR processors in nonhomogenous background, IEEE Trans. Aerosp. Electron. Syst., 24 (1988), 427-445. doi: 10.1109/7.7185.

[24]

D. A. Abraham and P. K. Willett, Active sonar detection in shallow water using the page test, IEEE Oceanic Eng., 27 (2002), 35-46. doi: 10.1109/48.989883.

[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), 3229-3233. doi: 10.1109/78.330381.

[26]

M. A. Mansour, B. V. Smith and J. A. Edwards, PC-based real-time active sonar simulator, IEE Proc.-Radar, Sonar Navig., 144 (1997), 227-233. doi: 10.1049/ip-rsn:19971260.

[27]

S. Stein, Algorithm for ambiguity function processing, IEEE Trans. Acoust. Speech Signal Proc., 29 (1981), 588-599. doi: 10.1109/TASSP.1981.1163621.

[28]

L. Auslander and I. Gertner, Wideband ambiguity function generation and $ax+b$ group, from Signal Processing, Part I: Signal Processing, Springer-Verlag, New York, 1 (1990), 1-12.

[29]

D. Alexandrou and C. D. Moustier, Adaptive noise canceling applied to sea beam sidelobe interference rejection, IEEE J. Oceanic Eng., 13 (1988), 70-76. doi: 10.1109/48.556.

[30]

O. Rioul and M. Vetterli, Wavelets and signal processing, IEEE Signal Process. Magazine, (1991), 14-38. doi: 10.1109/79.91217.

[31]

S. Mallat, "A Wavelet Tour of Signal Processing," 2nd edition, Academic Press, Uk. 1999.

[32]

R. Young, "Wavelet Theory and Its Applications," Kluwer Academic Publisher, Bosten. 1993.

[33]

R. P. Brent, "Algorithms for Minimization without Derivatives," Prentice-Hall, Englewood Cliffs, New Jersey. 1973.

[34]

D. Alexandrou, Signal recovery in a reverberation-limited environment, IEEE Journal of Oceanic Engineering, 12 (1987), 553-559. doi: 10.1109/JOE.1987.1145285.

[35]

J. Sadowsky, Investigation of signal characteristics using the continuous wavelet transform, Johns Hopkins APL Technical Digest, 17 (1996), 258-269.

[36]

M. Aineto and S. Lawson, Narrowband signal detection in a reverberation-limited environment, OCEAN'97. MTS/IEEE Proc., 1 (1997), 27-32.

[37]

M. Sugeno, "Industrial Applications of Fuzzy Control," Elsevier Science Pub. Co., 1985.

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