Radar Image Modelling and Detection Using Neural Networks

  • G. Hennessey
  • H. Leung
  • A. Drosopoulos


Apparently random behaviour of a deterministic nonlinear dynamical system is referred to as chaos. Chaotic systems arise naturally in many circumstances. If we assume that sea clutter is the result of a chaotic process, we can apply an alternative approach to clutter elimination in radar signals. A neural network can be used to model the underlying system dynamics; in this case a radial basis function (RBF) network used. The radar signal is input to the network, producing a single step prediction. An error signal is generated by comparing each network prediction with the next element of the actual radar signal. As a target will not conform to the same dynamics as the clutter, a large prediction error should be observed when a target is present in the signal. Classical detection schemes are applied to the error signal to implement target detection. This approach has been tested using data collected at Osborne Head, Nova Scotia, Canada, by an instrumental quality X-band coherent radar. Quantization error limits the prediction accuracy, but the RBF is capable of reaching the best prediction for both temporal data and spatial data produced by a radar sweep through a range of azimuth. The RBF predictive detector is shown to be efficient in detecting small targets in sea clutter.


Radial Basis Function Radial Basis Function Network Neural Radial Basis Function Network Radar Signal Large Prediction Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    T. Poggio and F. Girosi. Networks for approximation and learning, Proceedings of the IEEE, Vol. 78, No. 9, 1481–1496, 1990.CrossRefGoogle Scholar
  2. 2.
    T.M. Cover. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition, IEEE Transactions on Electronic Computers, Vol. EC-14, No. 3, 326–334, 1965.CrossRefzbMATHGoogle Scholar
  3. 3.
    D. Lowe and A.R. Webb. Time series prediction by adaptive networks: a dynamical systems perspective, IEE Proceeding F, Vol. 138, No. 1, 17–24, 1991.Google Scholar
  4. 4.
    S. Haykin. Neural Networks: A Comprehensive Foundation, Macmillan Canada, Toronto, 1994.zbMATHGoogle Scholar
  5. 5.
    W.A. Light. Some aspects of radial-basis function approximation, NATO ASI Series, Vol. 256, 163–190, Kluwer Academic Publishers, Boston, 1992.Google Scholar
  6. 6.
    G.H. Golub and C.F. Van Loan. Matrix Computations, 2nd ed., John Hopkins University Press, London, 1989.zbMATHGoogle Scholar
  7. 7.
    J. Park and I.W. Sandberg. Universal approximation using radial-basis function networks, Neural Computation, Vol. 3, 246–257, 1991.CrossRefGoogle Scholar
  8. 8.
    H. Leung and T. Lo. Chaotic radar signal processing over the sea, IEEE Journal of Oceanic Engineering, Vol. 18, 287–295, 1993.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • G. Hennessey
    • 1
  • H. Leung
    • 2
  • A. Drosopoulos
    • 2
  1. 1.PCI EnterprisesRichmond HillCanada
  2. 2.Defence Research Establishment OttawaOttawaCanada

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