Targets Detection in Sea Clutter Based on Echo State Network

Zhan Xu; Jian Wei Wan; Gang Li; Fang Su

doi:10.4028/www.scientific.net/AEF.4.255

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HomeAdvanced Engineering ForumAdvanced Engineering Forum Vol. 4Targets Detection in Sea Clutter Based on Echo...

Targets Detection in Sea Clutter Based on Echo State Network

Abstract:

A novel method to predict the sea clutter time series and detect target embedded in sea clutter is presented. The method is actually a recurrent neural network called an echo state network (ESN). A recursive least squares (RLS) algorithm is used for updating the output weights of ESN. A set of time series from IPIX radar data is tested. Numerical experiments reveal that the proposed network shows higher prediction precision in pure sea clutter data. Moreover, the mean squared error (MSE) between real-life data and prediction value by ESN can be used to detect target effectively.

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Periodical:

Advanced Engineering Forum (Volume 4)

Pages:

255-258

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.4.255

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Online since:

June 2012

Authors:

Zhan Xu, Jian Wei Wan, Gang Li, Fang Su

Keywords:

Echo State Network (ESN), Sea Clutter, Target Detection

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Creative Commons CC BY 4.0

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References

[1] H. Jaeger, The echo state approach to analysing and training recurrent neural networks. Tech. Rep. Fraunhofer Institute for Autonomous Intelligent Systens: German National Research Center for Information Technology (GMD Report 148) (2001).

Google Scholar

[2] H. Jaeger, Adaptive nonlinear system identification with echo state networks. In S. T. S. Becher, & K. Obermayer (Eds. ), Advances in neural information processing systems (pp.593-600). Cambrige, MA: MIT Press (2003).

Google Scholar

[3] H. Jarger and H. Haas, Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304(5667), 78-80 (2004).

DOI: 10.1126/science.1091277

Google Scholar

[4] K. Ishii, T. van der Zant, V. Becanovic, and P. Ploger, Identification of motion with echo state network. In Proc. OCEANS'04 (2004), pp.1205-1210.

DOI: 10.1109/oceans.2004.1405751

Google Scholar

[5] P.G. Ploger, A. Arghir, T. Gunther, and R. Hosseiny, Echo state networks for mobile robot modeling and control. In Proc. RoboCup (2003), pp.157-168.

DOI: 10.1007/978-3-540-25940-4_14

Google Scholar