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 & Management Optimization, 2013, 9 (1) : 205-225. doi: 10.3934/jimo.2013.9.205
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show all references

References:
[1]

Prentice Hall, 2nd Edition, 1991. Google Scholar

[2]

London: E&FN Spon, 2nd e/d, 1996. Google Scholar

[3]

Wiley, New York, 1968. Google Scholar

[4]

Proc. IEEE, 67 (1979), 920-930. doi: 10.1109/PROC.1979.11355.  Google Scholar

[5]

IEEE Trans. Military Elect., 9 (1965), 56-69. doi: 10.1109/TME.1965.4323176.  Google Scholar

[6]

e/4, McGraw Hill, Taipei, 2002.  Google Scholar

[7]

IEEE Signal Processing Magazine, (1994), 13-32. doi: 10.1109/79.252866.  Google Scholar

[8]

Multidimensional Systems and Signal Processing, 13 (2002), 157-186. doi: 10.1023/A:1014488726761.  Google Scholar

[9]

IEEE Trans. Inform. Theory, 37 (1991), 317-327. doi: 10.1109/18.75247.  Google Scholar

[10]

IEEE Trans. Information Theory, 23 (1977), 164-178. Google Scholar

[11]

IEEE Journal of Oceanic Engineering, 20 (1995), 80-84. 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]

IEEE Proc., 63 (1975), 1692-1716. Google Scholar

[15]

Pearson Education Taiwan Ltd., 2004. Google Scholar

[16]

Proc. IEEE Int. Conf. Fuzzy Sys., II (1993), 906-911. Google Scholar

[17]

Proc. IEEE Int. Conf. Fuzzy Sys., II (1994), 1342-1347. doi: 10.1109/FUZZY.1994.343617.  Google Scholar

[18]

e/4, Prentice-Hall, London. 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]

Neural Computation, 1 (1989), 270-280. doi: 10.1162/neco.1989.1.2.270.  Google Scholar

[21]

IEEE, Trans. Neural Networks, 3 (1992), 889-898. doi: 10.1109/72.165591.  Google Scholar

[22]

IEEE Trans. on Inf. Theory, 41 (1995), 613-627. doi: 10.1109/18.382009.  Google Scholar

[23]

IEEE Trans. Aerosp. Electron. Syst., 24 (1988), 427-445. doi: 10.1109/7.7185.  Google Scholar

[24]

IEEE Oceanic Eng., 27 (2002), 35-46. doi: 10.1109/48.989883.  Google Scholar

[25]

IEEE Trans. Signal Processing, 42 (1994), 3229-3233. doi: 10.1109/78.330381.  Google Scholar

[26]

IEE Proc.-Radar, Sonar Navig., 144 (1997), 227-233. doi: 10.1049/ip-rsn:19971260.  Google Scholar

[27]

IEEE Trans. Acoust. Speech Signal Proc., 29 (1981), 588-599. doi: 10.1109/TASSP.1981.1163621.  Google Scholar

[28]

from Signal Processing, Part I: Signal Processing, Springer-Verlag, New York, 1 (1990), 1-12.  Google Scholar

[29]

IEEE J. Oceanic Eng., 13 (1988), 70-76. doi: 10.1109/48.556.  Google Scholar

[30]

IEEE Signal Process. Magazine, (1991), 14-38. doi: 10.1109/79.91217.  Google Scholar

[31]

2nd edition, Academic Press, Uk. 1999.  Google Scholar

[32]

Kluwer Academic Publisher, Bosten. 1993.  Google Scholar

[33]

Prentice-Hall, Englewood Cliffs, New Jersey. 1973.  Google Scholar

[34]

IEEE Journal of Oceanic Engineering, 12 (1987), 553-559. doi: 10.1109/JOE.1987.1145285.  Google Scholar

[35]

Johns Hopkins APL Technical Digest, 17 (1996), 258-269. Google Scholar

[36]

OCEAN'97. MTS/IEEE Proc., 1 (1997), 27-32. Google Scholar

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Elsevier Science Pub. Co., 1985. Google Scholar

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