Sea clutter constituent synthesis approach based on a new decomposition model

In this paper, a sea clutter decomposition model is newlxy proposed. The decomposition structure is organized according to a comparison study between measured sea clutter and Lorenz chaotic signals. Based on the decomposition model, a sea clutter constituent synthesis approach is developed to reconstruct sea clutter series with neural networks. Simulation results demonstrate the effectiveness and stability of the proposed approach.

[1]  Henry Chan,et al.  Radar sea-clutter at low grazing angles , 1990 .

[2]  Bernard Mulgrew,et al.  Nonlinear prediction of chaotic signals using a normalised radial basis function network , 2002, Signal Process..

[3]  J. Mason,et al.  Algorithms for approximation , 1987 .

[4]  C. Baker K-distributed coherent sea clutter , 1991 .

[5]  G. Davidson Simulation of coherent sea clutter , 2010 .

[6]  Henry Leung,et al.  Reconstruction of piecewise chaotic dynamic using a genetic algorithm multiple model approach , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Bruce A. Whitehead,et al.  Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction , 1996, IEEE Trans. Neural Networks.

[8]  James J. Carroll,et al.  Approximation of nonlinear systems with radial basis function neural networks , 2001, IEEE Trans. Neural Networks.

[9]  Nan He,et al.  Chaotic modelling of sea clutter , 1992 .

[10]  H. Leung,et al.  Chaotic radar signal processing over the sea , 1993 .

[11]  S. Watts,et al.  Radar Detection Prediction in K-Distributed Sea Clutter and Thermal Noise , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Bernie Mulgrew,et al.  False detection of chaotic behaviour in the stochastic compound k-distribution model of radar sea clutter , 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

[13]  Jing Hu,et al.  TARGET DETECTION WITHIN SEA CLUTTER: A COMPARATIVE STUDY BY FRACTAL SCALING ANALYSES , 2006 .

[14]  Fulvio Gini Performance analysis of two structured covariance matrix estimators in compound-Gaussian clutter , 2000, Signal Process..

[15]  Xiao-Ke Xu,et al.  Low Observable Targets Detection by Joint Fractal Properties of Sea Clutter: An Experimental Study of IPIX OHGR Datasets , 2010, IEEE Transactions on Antennas and Propagation.

[16]  H. Leung,et al.  Chaotic behaviour and non-linear prediction of airborne radar sea clutter data , 2002, Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322).

[17]  S. Haykin,et al.  Neural network modeling of radar backscatter from an ocean surface using chaos theory , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[18]  Simon Haykin,et al.  Uncovering nonlinear dynamics-the case study of sea clutter , 2002, Proc. IEEE.

[19]  Alan V. Oppenheim,et al.  Quasi-Orthogonal Wideband Radar Waveforms Based on Chaotic Systems , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Jian Guan,et al.  Fractal characteristic in frequency domain for target detection within sea clutter , 2012 .

[21]  S. Haykin Adaptive Radar Signal Processing , 2013 .

[22]  L. Jofre,et al.  A Dual-Linearly-Polarized MEMS-Reconfigurable Antenna for Narrowband MIMO Communication Systems , 2010, IEEE Transactions on Antennas and Propagation.

[23]  Mohammad S. Sharawi,et al.  High Fidelity Antenna Model Validation Results of a GNSS Multipath Limiting Antenna , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[24]  P. R. Chakravarthi Radar target detection in chaotic clutter , 1997, Proceedings of the 1997 IEEE National Radar Conference.

[25]  Marco Lops,et al.  Modelling and simulation of non-Rayleigh radar clutter , 1991 .

[26]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[27]  S. Haykin,et al.  Chaos, sea clutter, and neural networks , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[28]  Henry Leung,et al.  Prediction of noisy chaotic time series using an optimal radial basis function neural network , 2001, IEEE Trans. Neural Networks.

[29]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[30]  Henry Leung,et al.  A multiple-model prediction approach for sea clutter modeling , 2003, IEEE Trans. Geosci. Remote. Sens..

[31]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .