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

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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

Abstract
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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 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.
[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.
[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.
[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, ().
[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.
[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.
[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, ().
[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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