Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

Authors

  • Jesús Concepción Bacallao Vidal Polytechnic José Antonio Echeverría image/svg+xml
  • José Raúl Machado Fernández Polytechnic José Antonio Echeverría image/svg+xml

DOI:

https://doi.org/10.9781/ijimai.2016.421

Keywords:

Artificial Neural Networks, Estimation, Pareto Distributed Clutter

Abstract

The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE). The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.

Downloads

Download data is not yet available.

References

[1] H. Meikle, Modern Radar Systems, 2nd Edition ed.: Artech House 2008.

[2] W. L. Melvin and J. A. Scheer, Principles of Modern Radar, Vol III Radar Applications: Scitech Publishing, 2014.

[3] K. Ward, R. Tough, and S. Watts, Sea Clutter Scattering, the K Distribution and Radar Performance, 2nd ed. London, United Kingdom: The Institution of Engineering and Technology, 2013.

[4] L. Rosenberg, “The effect of temporal correlation with K and KK distributed Sea-Clutter”, presented at the IEEE Radarcon Conference, 2012.

[5] E. Ollila, E. Tayler, D. E. Koivumen, and V. Poor, “Compound-Gaussian Clutter Modeling with an Inverse Gaussian texture distribution”, IEEE Transactions on Signal Processing Letter, vol. 19(12), pp. 876-879, 2012.

[6] L. Rosenberg, D. J. Crisp, and N. J. Stacy, “Analysis of the KK distribution with Medium Grazing Angle Sea-clutter”, in IET Proceedings of Radar Sonar and Navigation, vol. 4, pp. 209-222, 2010.

[7] D. J. Crisp, L. Rosenberg, and N. J. Stacy, “Modelling X-band Sea-Clutter with the K distribution: Shape Parameter Variation”, presented at the Radar International Conference “Surveillance for a Safer World, 2009.

[8] M. Sekine, M. P. S. Musha, Y. Yomita, T. Hagisawa, T. Iraby, and E. Kiuchi, “Log-Weibull Distributed Sea Clutter”, IEE Proceedings, vol. 127(3), 1980.

[9] H. C. Chan, “Radar Sea-Clutter at Low Grazing Angles”, IEE Proceedings, vol. 137(2), 1990.

[10] T. J. Nohara and S. Haykin, “Canadian East Coast Radar Trials and the K-Distribution”, in IEE Proceedings on Radar and Signal Processing, vol. 131(2), pp. 80-88, 1991.

[11] Q. Ping, “Analysis of Ocean Clutter for Wide-Band Radar Based on Real Data”, in Proceedings of the 2011 International Conference on Innovative Computing and Cloud Computing, Wuhan, China, 2011, pp. 121-124.

[12] Z. Chen, X. Liu, Z. Wu, and X. Wang, “The Analysis of Sea Clutter Statistics Characteristics Based on the Observed Sea Clutter of Ku-Band Radar”, in IEEE Proceedings of the International Symposium on Antennas & Propagation, vol. 2, pp. 1183-1186, 2013.

[13] S. Ishii, S. Sayama, and K. Mizutani, “Effect of Changes in Sea-Surface State on Statistical Characteristics of Sea Clutter with X-band Radar”, Wireless Engineering and Technology, vol. 2(3), pp. 175-183, 2011.

[14] Y. Dong, “Distribution of X-Band High Resolution and High Grazing Angle Sea Clutter, Technical Report DSTO-RR-0316,” Electronic Warfare and Radar Division, Defence Science and Technology Organization, Edinburgh, South Australia, 2006.

[15] S. Watts and L. Rosenberg, “A Review of High Grazing Angle Sea Clutter”, presented at the IEEE 2013 International Conference on Radar, pp. 240-245, 2013.

[16] M. Farshchian and F. L. Posner, “The Pareto Distribution for Low Grazing Angle and High Resolution X-Band Sea Clutter”, in IEEE Radar Conference Proceedings, pp. 789-793, 2010.

[17] G. V. Weinberg, “Coherent Multilook Radar Detection for Targets in Pareto Distributed Clutter, Technical Report DSTO-TR-2646”, Electronic Warfare and Radar Division, Defence Science and Technology Organisation, Edinburgh, South Australia, 2012.

[18] G. V. Weinberg and D. Finch, “Analysis of a Pareto Mixture Distribution for Maritime Surveillance Radar”, Journal of Electrical and Computer Engineering, 2012.

[19] G. V. Weinberg, “Assessing Detector Performance, with Application to Pareto Coherent Multilook Radar Detection”, IET Radar, Sonar and Navigation, vol. 7, pp. 401-412, 2013.

[20] G. V. Weinberg, “Constant False Alarm Rate Detectors for Pareto Clutter Models”, IET Radar, Sonar and Navigation, vol. 7, pp. 153-163, 2013.

[21] G. V. Weinberg, “Estimation of Pareto clutter parameters using order statistics and linear regression”, IET Electronics Letters 20th, vol. 49(13), pp. 845- 846, 2013.

[22] G. V. Weinberg, “Constant False Alarm Rate Detection in Pareto Distributed Clutter: Further Results and Optimality Issues”, Contemporary Engineering Sciences, vol. 7(6), pp. 231-261, 2014.

[23] P. J. Chung, W. J. J. Roberts, and J. F. Bohme, “Recursive K distribution parameter estimation”, IEEE Transactions on Signal Processing, vol. 53(2), pp. 397-402, 2005.

[24] A. Dogandzic, A. Nehorai, and J. Wang, “Maximum likelihood estimation of compound-Gaussian clutter and target parameters”, in Proceedings of 12th Annual Workshop Adaptive Sensor, Array Processing(ASAP ’04), Lincoln Laboratory,, Lexington, MA, 2004.

[25] A. Mezache, M. Sahed, T. Laroussi, and D. Chicouche, “Two novel methods for estimating the compound K clutter parameters in presence of thermal noise”, IET Radar Sonar Navig., vol. 5(9), pp. 934-942, 2011.

[26] A. Mezache and M. Sahed, “Parameter Estimation in K-Distributed Clutter with Noise using Nonlinear Networks”, Université de Constantine, Faculté des Sciences de l’Ingénieur, 2010.

[27] I. B. Aban, M. M. Meerschaert, and A. K. Panorska, “Parameter estimation for the truncated pareto distribution”, Journal of the American Statistical Association, vol. 101(473), pp. 270–277, 2006.

[28] E. Chlebus and R. Ohri, “Estimating parameters of the pareto distribution by means of zipf’s law: application to internet research”, in IEEE Globecom, 2005, pp. 1039–1043.

[29] G. V. Weinberg, “An Investigation of the Pareto Distribution as a Model for High Grazing Angle Clutter, DSTO-TR-2525”, Electronic Warfare and Radar Division, Defence Science and Technology Organisation, Edinburgh, South Australia, 2011.

[30] J. R. Machado Fernández, J. C. Bacallao Vidal, and N. Chávez Ferry, “A Neural Network Approach to Weibull Distributed Sea Clutter Parameter’s Estimation”, Inteligencia Artificial vol. 18(56), pp. 3-13, 2015.

[31] J. R. Machado Fernández and J. C. Bacallao Vidal, “Improved Shape Parameter Estimation in K Clutter with Neural Networks (accepted)”, International Journal of Interactive Multimedia and Artificial Intelligence, 2016.

[32] A. V. Asimit, E. Furman, and R. Vernic, “On a multivariate pareto distribution”, Insurance, vol. 46(2), pp. 308–316, 2010.

[33] M. Rytgaard, “Estimation in the pareto distribution”, ASTIN Bulletin, vol. 20(2), pp. 201–216, 1990.

[34] J. M. Gelb, R. E. Heath, and G. L. Tipple, “Statistics of Distinct Clutter Classes in Midfrequency Active Sonar”, IEEE Oceanic Eng, vol. 35, pp. 220-229, 2010.

[35] P. R. Chakravarthi and A. Ozturk, “On Determining the Radar Threshold from Experimental Data”, Conference on Signals, Systems and Computers., pp. 594-598, 1991.

[36] M. Piotrkowski, “Some Preliminary Experiments with DistributionIndependednt EVT-CFAR based on Recorded Radar Data”, presented at the IEEE Radar Conference 08, 2008.

[37] A. N. O’Connor, Probability Distributions Used in Reliability Engineering: University of Maryland, 2011.

[38] J. Metcalf, S. D. Blunt, and B. Himed, “A Machine Learning Approach to Cognitive Radar Detection”, 2015 IEEE Radar Conference (RadarCon), pp. 1405-1411, 2015.

[39] D. Ling and W. Pingjun, “A Method for Determining Scale Factor of CFAR Detector Based on BP Neural Networks”, presented at the The 2nd International Conference on Computer Application and System Modeling, 2012.

[40] J. A. Garzón Guerrero, “Clasificación de blancos de radar en ambientes de ruido arbitrario mediante resonancias naturales y técnicas de componentes principales”, Doctor en Ciencias, Universidad de Granada, 2012.

[41] N. B. Gálvez and J. E. Cousseau, “Improved Neural Network Based CFAR Detection for non Homogeneous Background and Multiple Target Situations”, Latin American Applied Research, vol. 42, pp. 343-350, 2012.

[42] D. Kriesel, A Brief Introduction to Neural Networks: Springer, 2005.

[43] M. H. Beale, M. T. Hagan, and H. B. Demuth, Neural Network Toolbox™ User’s Guide: Mathworks, 2010.

[44] J. L. Ticknor, “A Bayesian regularized artificial neural network for stock market forecasting”, Expert Systems with Applications, vol. 40(14), pp. 5501–5506, 2013.

[45] C. Forbes, M. Evans, N. Hastings, and B. Peacok, Statistical Distributions, 4th ed.: Wiley, 2011.

[46] J. R. Machado Fernández, “Estimation of the Relation between Weibull Distributed Sea clutter and the CA-CFAR Scale Factor”, Journal of Tropical Engineering, vol. 25(2), pp. 19-28, 2015.

[47] J. R. Machado Fernández, “Weibull Clutter Assumption Neural Adaptive Threshold Estimation Cell Averaging Constant False Alarm Rate (W-NATE-CA-CFAR) Detector (under revision)”, Ingeniare, 2015.

[48] J. M. Fialkowski, R. C. Gauss, and D. M. Drumheller, “Measurements and Modeling of Low-Frequency Near-Surface Scattering Statistics”, IEEE Journal of Oceanic Engineering, vol. 29(2), pp. 197-214, 2004.

[49] M. Fingas, Handbook of Oil Spill Science and Technology: WILEY, 2015.

[50] C.-h. Qi and Z.-q. Zhao, “Electromagnetic Scattering and Statistic Analysis of Clutter from Oil Contaminated Sea Surface”, Radioengineering, vol. 24(1), 2015.

[51] D. Abraham, A. and J. R. Preston, “Statistical Analysis of Monostatic and Bistatic Echoes from ShipWreck”, IEEE Journal of Oceanic Engineering, vol. 25(1), 2010.

[52] J. R. Preston and D. A. Abraham, “Statistical Analysis of Multistatic Echoes From a Shipwreck in the Malta Plateau”, IEEE Journal of Oceanic Engineering, vol. 40(3), pp. 643-656, 2015.

[53] J. C. Bacallao Vidal, “Un modelo Teórico de la Técnica DRACEC. Metodología del Proceso de Adaptación al Fondo”, Doctor en Ciencias Técnicas, Instituto Técnico Militar “José Martí”, La Habana, Cuba, 2003.

Downloads

Published

2016-12-01
Metrics
Views/Downloads
  • Abstract
    56
  • PDF
    18

How to Cite

Bacallao Vidal, J. C. and Machado Fernández, J. R. (2016). Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks. International Journal of Interactive Multimedia and Artificial Intelligence, 4(2), 7–11. https://doi.org/10.9781/ijimai.2016.421

Issue

Vol. 4 No. 2 (2016): IJIMAI 2016 - Regular Issue.

Section

Regular Articles