Performance Evaluation of an ANC-based Hybrid Algorithm for Multi-target Wideband Active Sonar Echolocation System
This paper evaluates performances of an adaptive noise
cancelling (ANC) based target detection algorithm on a set of real test
data supported by the Defense Evaluation Research Agency (DERA
UK) for multi-target wideband active sonar echolocation system. The
hybrid algorithm proposed is a combination of an adaptive ANC
neuro-fuzzy scheme in the first instance and followed by an iterative
optimum target motion estimation (TME) scheme. The neuro-fuzzy
scheme is based on the adaptive noise cancelling concept with the
core processor of ANFIS (adaptive neuro-fuzzy inference system) to
provide an effective fine tuned signal. The resultant output is then
sent as an input to the optimum TME scheme composed of twogauge
trimmed-mean (TM) levelization, discrete wavelet denoising
(WDeN), and optimal continuous wavelet transform (CWT) for
further denosing and targets identification. Its aim is to recover the
contact signals in an effective and efficient manner and then determine
the Doppler motion (radial range, velocity and acceleration) at very
low signal-to-noise ratio (SNR). Quantitative results have shown that
the hybrid algorithm have excellent performance in predicting targets-
Doppler motion within various target strength with the maximum
false detection of 1.5%.
[1] R. Young, Wavelet Theory and Its Applications, Kluwer Academic
Publisher, Bosten, 1993.
[2] L. G. Weiss, "Wavelets and wideband correlation processing," IEEE
Signal Processing Magazine, pp. 13-32, 1994.
[3] H. Naparst, "Dense target signal processing," IEEE Trans. Inform.
Theory, vol. 37, pp. 317-327, 1991.
[4] P. Delaney and D. Walsh, "Performance analysis of the incoherent and
skewness matched filter detectors in multioath environments," IEEE
Journal of Oceanic Engineering, vol. 20, no. 1, pp. 80-84, 1995.
[5] C. H. Tseng and M. Cole, "Towards smart-pixel-based implementation
of wideband active sonar echolocation system for multi-target detection,"
ICSPCS2008, Gold Coast, Australia, 2008.
[6] C. H. Tseng, and M. Cole,"Optimum multi-target detection using
an ANC neuro-fuzzy scheme and wideband replica correlator," IEEE
ICASSP2009, pp. 1369-1372, Taipei, Taiwan.
[7] C. H. Tseng, "Effective wideband acoustic sonar signal detection based
on adaptive neuro-fuzzy processor and optimal wavelet transform," The
17th National Conference on Fuzzy Theory and Its Application, pp.
817-822, Kaohsiung, 2009.
[8] B. Widrow et al., "Adaptive noise cancelling: Principles and applications,"
IEEE proc, vol. 63, pp. 1692-1716, 1975.
[9] 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.
[10] S. Mallat, A Wavelet Tour of Signal Processing, 2/e, Academic Press,
UK, 1999.
[11] R. P. Brent, Algorithms for Minimization without Derivatives, Prentice-
Hall, Englewood Cliffs, New Jersey, 1973
[12] M. A. Mansour, B. V. Smith, and J. A. Edwards, "PC-based real-time
active sonar simulator," IEE Proc.-Radar, Sonar Navig., vol. 144, pp.
227-233, 1997.
[13] M. Sugeno, "Industrial applications of fuzzy control," Elsevier Science
Pub. Co., 1985.
[14] D. L. Donoho, "De-Noising by soft-thresholding," IEEE Trans. on Inf.
Theory, vol. 41, 3, pp. 613-627, 1995.
[1] R. Young, Wavelet Theory and Its Applications, Kluwer Academic
Publisher, Bosten, 1993.
[2] L. G. Weiss, "Wavelets and wideband correlation processing," IEEE
Signal Processing Magazine, pp. 13-32, 1994.
[3] H. Naparst, "Dense target signal processing," IEEE Trans. Inform.
Theory, vol. 37, pp. 317-327, 1991.
[4] P. Delaney and D. Walsh, "Performance analysis of the incoherent and
skewness matched filter detectors in multioath environments," IEEE
Journal of Oceanic Engineering, vol. 20, no. 1, pp. 80-84, 1995.
[5] C. H. Tseng and M. Cole, "Towards smart-pixel-based implementation
of wideband active sonar echolocation system for multi-target detection,"
ICSPCS2008, Gold Coast, Australia, 2008.
[6] C. H. Tseng, and M. Cole,"Optimum multi-target detection using
an ANC neuro-fuzzy scheme and wideband replica correlator," IEEE
ICASSP2009, pp. 1369-1372, Taipei, Taiwan.
[7] C. H. Tseng, "Effective wideband acoustic sonar signal detection based
on adaptive neuro-fuzzy processor and optimal wavelet transform," The
17th National Conference on Fuzzy Theory and Its Application, pp.
817-822, Kaohsiung, 2009.
[8] B. Widrow et al., "Adaptive noise cancelling: Principles and applications,"
IEEE proc, vol. 63, pp. 1692-1716, 1975.
[9] 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.
[10] S. Mallat, A Wavelet Tour of Signal Processing, 2/e, Academic Press,
UK, 1999.
[11] R. P. Brent, Algorithms for Minimization without Derivatives, Prentice-
Hall, Englewood Cliffs, New Jersey, 1973
[12] M. A. Mansour, B. V. Smith, and J. A. Edwards, "PC-based real-time
active sonar simulator," IEE Proc.-Radar, Sonar Navig., vol. 144, pp.
227-233, 1997.
[13] M. Sugeno, "Industrial applications of fuzzy control," Elsevier Science
Pub. Co., 1985.
[14] D. L. Donoho, "De-Noising by soft-thresholding," IEEE Trans. on Inf.
Theory, vol. 41, 3, pp. 613-627, 1995.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49555", author = "Jason Chien-Hsun Tseng", title = "Performance Evaluation of an ANC-based Hybrid Algorithm for Multi-target Wideband Active Sonar Echolocation System", abstract = "This paper evaluates performances of an adaptive noise
cancelling (ANC) based target detection algorithm on a set of real test
data supported by the Defense Evaluation Research Agency (DERA
UK) for multi-target wideband active sonar echolocation system. The
hybrid algorithm proposed is a combination of an adaptive ANC
neuro-fuzzy scheme in the first instance and followed by an iterative
optimum target motion estimation (TME) scheme. The neuro-fuzzy
scheme is based on the adaptive noise cancelling concept with the
core processor of ANFIS (adaptive neuro-fuzzy inference system) to
provide an effective fine tuned signal. The resultant output is then
sent as an input to the optimum TME scheme composed of twogauge
trimmed-mean (TM) levelization, discrete wavelet denoising
(WDeN), and optimal continuous wavelet transform (CWT) for
further denosing and targets identification. Its aim is to recover the
contact signals in an effective and efficient manner and then determine
the Doppler motion (radial range, velocity and acceleration) at very
low signal-to-noise ratio (SNR). Quantitative results have shown that
the hybrid algorithm have excellent performance in predicting targets-
Doppler motion within various target strength with the maximum
false detection of 1.5%.", keywords = "Wideband Active Sonar Echolocation, ANC Neuro-Fuzzy, Wavelet Denoise, CWT, Hybrid Algorithm.", volume = "4", number = "9", pages = "1322-7", }