Vol. 21
Latest Volume
All Volumes
PIERC 151 [2025] PIERC 150 [2024] PIERC 149 [2024] PIERC 148 [2024] PIERC 147 [2024] PIERC 146 [2024] PIERC 145 [2024] PIERC 144 [2024] PIERC 143 [2024] PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2011-05-27
Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures.
By
Progress In Electromagnetics Research C, Vol. 21, 243-255, 2011
Abstract
Non-Cooperative Target Recognition (NCTR) of aircrafts from radar measurements is a formidable problem that has drawn the attention of engineers and scientists over the last years. NCTR techniques typically involve a database with a huge amount of information from different known targets and a reliable identification algorithm able to highlight the likeness between measured and stored data. This paper uses High Resolution Range Profiles produced with a high-frequency software tool to train Arti cial Neural Networks for distinguishing between different classes of aircrafts. Actual data from the ORFEO measurement campaign are used to assess the performance of the trained networks.
Citation
Antonio Jurado-Lucena, Ignacio Montiel-Sanchez, David Escot-Bocanegra, Raul Fernandez-Recio, and David Poyatos-Martınez, "Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures.," Progress In Electromagnetics Research C, Vol. 21, 243-255, 2011.
doi:10.2528/PIERC11030206
References

1. Schiller, J. and R. L. Cranos, "Non-cooperative air target identification using radar," RTO MP-06, Mannheim, Germany, April 22-24, 1998.

2. Cohen, M. N., "An overview of radar-based automatic non cooperative target recognition techniques," IEEE International Conference on Systems Engineering, 29-34, 1991.
doi:10.1109/ICSYSE.1991.161074

3. Rosenbach, R. and J. Schiller, "Non-cooperative air target identi¯cation using radar imagery: indentification rate as a function of signal bandwidth," IEEE International Radar Conference, 305-309, 2000.

4. Conde, O. M., J. Perez, and M. F. Catedra, "Stationary phase method application for the analysis of radiation of complex 3d conducting structures," IEEE Transactions on Antennas and Propagation, Vol. 49, No. 5, 724-731, 2001.
doi:10.1109/8.929626

5. Wehner, R. D., High Resolution Radar, 2nd Ed., Artech House, 1995.

6. Heiden, R., Aircraft recognition with radar range profiles, Ph.D. Thesis, University of Amsterdam, The Netherlands, 1998.

7. Hsueh-Jyh, L., "Using range profiles as feature vectors to identify aerospace objects," IEEE Transactions on Antennas and Propagation, Vol. 41, No. 3, 261-268, 1993.
doi:10.1109/8.233138

8. Zyweck, A. and R. E. Bogner, "Radar target classification of commercial aircraft," IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, No. 2, 598-606, 1996.
doi:10.1109/7.489504

9. Liu, J., J. Zhang, and F. Zhao, "Feature for distinguishing propeller-driven airplanes from turbine-driven airplanes," IEEE Transactions on Aerospace and Electronic Systems, Vol. 46, No. 1, 222-229, 2010.
doi:10.1109/TAES.2010.5417158

10. Lee, K. C., C. W. Huang, and M. C. Fang, "Radar target recognition by projected features of frequency-diversity RCS," Progress In Electromagnetics Research, Vol. 81, 121-133, 2008.
doi:10.2528/PIER08010206

11. Han, S. K. and H. T. Kim, "Efficent radar target recognition of range profile and time-frequency analysis," Progress In Electromagnetics Research, Vol. 108, 131-140, 2010.
doi:10.2528/PIER10071601

12. Roth, M. W., "Survey of neural network technology for automatic target recognition," IEEE Transactions on Neural Networks, Vol. 1, No. 1, 28-43, 1990.
doi:10.1109/72.80203

13. Jouny, I., F. D. Garber, and S. C. Ahalt, "Classification of radar targets using synthetic neural networks," IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 2, 336-344, 1993.
doi:10.1109/7.210072

14. Heiden, R. and J. Vries, TheORFEOMeasurementCampaign, TNO Defence, Security and Safety Report, FELSTAR-96-A073, 1996.

15. Farin, G., Curves and Surfaces for CAGD.: A Practical Guide, Morgan Kaufman Publishers, 2002.

16. Montiel, I., D. Poyatos, I. Gonzalez, D. Escot, C. Garcia, and E. Diego, "FASCRO code and the synthetic database generation problem," Proc. of SET-080 Target Identification and Recognition Using RF System, Oslo, Norway, 2004.

17. Escot-Bocanegra, D., D. Poyatos-Martinez, R. Fernandez-Recio, A. Jurado-Lucena, and I. Montiel-Sanchez, "New bench-mark radar targets for scattering analysis and electromagnetic software validation," Progress In Electromagnetics Research, Vol. 88, 39-52, 2008.
doi:10.2528/PIER08102201