The Separation of Radar Clutters using Multi-Layer Perceptron
Subject Areas : Communication Systems & DevicesMohammad Akhondi Darzikolaei 1 , Ataollah Ebrahimzadeh 2 , Elahe Gholami 3
1 - Babol Noshirvani university of technology
2 - Babol Noshirvani university of technology
3 - Babol Noshirvani university of technology
Keywords: Clutter, Classifier, Feature, Neural Network, Radar,
Abstract :
Clutter usually has negative influence on the detection performance of radars. So, the recognition of clutters is crucial to detect targets and the role of clutters in detection cannot be ignored. The design of radar detectors and clutter classifiers are really complicated issues. Therefore, in this paper aims to classify radar clutters. The novel proposed MLP-based classifier for separating radar clutters is introduced. This classifier is designed with different hidden layers and five training algorithms. These training algorithms consist of Levenberg-Marquardt, conjugate gradient, resilient back-propagation, BFGS and one step secant algorithms. Statistical distributions are established models which widely used in the performance calculations of radar clutters. Hence In this research, Rayleigh, Log normal, Weibull and K-distribution clutters are utilized as input data. Then Burg’s reflection coefficients, skewness and kurtosis are three features which applied to extract the best characteristics of input data. In the next step, the proposed classifier is tested in different conditions and the results represent that the proposed MLP-based classifier is very successful and can distinguish clutters with high accuracy. Comparing the results of proposed technique and RBF-based classifier show that proposed method is more efficient. The results of simulations prove that the validity of MLP-based method.
[1] J. Anderson, M. T. Gately, P. A. Penz, D. R. Collins, and others, “Radar signal categorization using a neural network,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1646–1657, 1990#
[2] J.-H.Lee, I.-S.Choi ,H.-T.Kim, “Natural frequency-based neural network approach to radar target recognition,” IEEE Trans. Signal Process., vol. 51, no. 12, p. 3191, December 2003#
[3] R. Rouveure, P. Faure, and Monod, “Multi-layer feed-forward perceptron for microwave signals processing,” in Geoscience and Remote Sensing, Symposium, 2003. IGARSS '03, vol. 6, 2003, pp. 3519-3521.#
[4] O. L. Mangasarian and W. H. Wolberg, “Cancer diagnosis via linear programming. University of Wisconsin-Madison,” Computer Sciences Department, 1990.#
[5] T. L. Fine, Feedforward neural network methodology. Springer Science & Business Media, 2006.#
[6] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115–133, 1943.#
[7] S. Mirjalili, S. Z. M. Hashim, and H. M. Sardroudi, “Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm,” Applied Mathematics and Computation, vol. 218, no. 22, pp. 11125–11137, 2012.#
[8] S. Mirjalili and S. Z. M. Hashim, “A new hybrid PSOGSA algorithm for function optimization,” in Computer and information application (ICCIA), 2010 international conference on, 2010, pp. 374–377.#
[9] Z. X. Guo, W. K. Wong, and M. Li, “Sparsely connected neural network-based time series forecasting,” Information Sciences, vol. 193, pp. 54–71, 2012.#
[10] P. Auer, H. Burgsteiner, and W. Maass, “A learning rule for very simple universal approximators consisting of a single layer of perceptrons,” Neural Networks, vol. 21, no. 5, pp. 786–795, 2008.#
[11] Haykin and C. Deng, “Classification of radar clutter using NN,” IEEE Trans. Neural Netw., vol. 2, November 1991.#
[12] R. Soleti, L. Cantini, F. Berizzi, A. Capria, and D. Calugi, “Neural Network for polarimetric radar target classification,” in Signal Processing Conference, 2006 14th European, 2006, pp. 1–5.#
[13] K. Cheikh and F. Soltani, “Application of neural networks to radar signal detection in k-distributed clutter,” Radar, Sonar and Navigation, IEE Proceedings, vol. 153, no. 5, pp. 460-466, 2006.#
[14] R. Vicen-Bueno, M. Rosa-Zurera, M. P. Jarabo-Amores, and R. Gil-Pita, “Automatic target detection in simulated ground clutter (Weibull distributed) by multilayer perceptrons in a low-resolution coherent radar,” Radar, Sonar & Navigation, IET, vol. 4, no. 2, pp. 315–328, 2010.#
[15] C. Bouvier, L. Martinet, G. Favier, H. Sedano, and M. Artaud, “Radar clutter classification using autoregressive modelling, K-distribution and neural network,” in Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on, 1995, vol. 3, pp. 1820–1823.#
[16] L. Pierucci and L. Bocchi, “Improvements of radar clutter classification in air traffic control environment,” in Signal Processing and Information Technology, 2007 IEEE International Symposium on, 2007, pp. 721–724.#
[17] L. Teng, H. Dan, “Model for spatial correlated clutter and its application to temporal spatialcorrelated clutter”, IET Microwaves, Antennas & Propagation, Vol. 5, No. 3, 2011, pp. 298-304.#
[18] K. D. Ward, S. Watts, and R. J. Tough, Sea clutter: scattering, the K distribution and radar performance, vol. 20. IET, 2006.#
[19] A. Farina, A. Russo, and F. A. Studer, “Coherent radar detection in log-normal clutter,” Communications, Radar and Signal Processing, IEE Proceedings F, vol. 133, no. 1, pp. 39–53, 1986.#
[20] W. Stehwien, “Parametric spectral analysis of radar clutter,” McMaster University, 1983.#
[21] J. P. Burg, “A new analysis technique for time series data,” presented at the NATO Advanced Study Institute on Signal Processing with Emphasis on Underwater Acoustics, Enschede, The Netherlands, 1968.#
[22] S. S. Haykin, Adaptive filter theory. Pearson Education India, 2008.#
[23] J. R. Barry, B. K. Carter, R. J. Erdahl, R. L. Harris, J. T. Miller, G. D. Smith, and R. M. Barnes, “Angel clutter and the ASR air traffic control radar,” Applied Physics Laboratory, John Hopkins University, Silver Spring, MD, Final Report under Federal Aviation Administration Contract DOT-FA72WA-2705, 1973.#
[24] M. Kubat, Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. Cambridge Univ Press, 1999.#
[25] H. Adeli and S.-L. Hung, Machine learning: neural networks, genetic algorithms, and fuzzy systems. John Wiley & Sons, Inc., 1994.#
[26] J. J. Moré, “The Levenberg-Marquardt algorithm: implementation and theory,” in Numerical analysis, Springer, 1978, pp. 105–116.#
[27] M. T. Hagan and M. B. Menhaj, “Training feedforward networks with the Marquardt algorithm,” Neural Networks, IEEE Transactions on, vol. 5, no. 6, pp. 989–993, 1994.#
[28] R. Fletcher and C. M. Reeves, “Function minimization by conjugate gradients,” The computer journal, vol. 7, no. 2, pp. 149–154, 1964.#
[29] A. Ebrahimzadeh, A. Khazaee, An efficient technique for classification of electrocardiogram signals, Advances in Electrical and Computer Engineering, Volume 9, 2009, pp. 89-93.#
[30] M. Riedmiller and H. Braun, “A direct adaptive method for faster backpropagation learning: The RPROP algorithm,” in Neural Networks, 1993., IEEE International Conference on, 1993, pp. 586–591.#
[31] S. Haykin, Neural Networks: A Comprehensive Foundation. New York: MacMillan, 1999.#
