Identifying Radar Targets using the GMDH Deep Neural Network

Document Type : Original Article

Authors

1 PhD student of Electronic Warfare Shahid Sattari University of Medical Sciences

2 PhD student of Electronic Warfare, Imam Hossein University,Email

3 Associate Professor, Imam Hossein University

Abstract

Radar is an electromagnetic device used to detect and determine the position of targets. The most basic task of radar is to extract information about the target by measuring the electromagnetic field characteristics of the return waves from the target. The radar environment of each country is one of the security and strategic areas of each country. Maintaining the security of this environment and identifying its goals can be one of the important requirements. Challenges and problems such as inaccuracy and inaccuracy of detection and high error are raised in the detection of radar targets. Various methods have been proposed so far, such as techniques based on target natural intensification frequencies, reversible signal polarization, machine learning methods, etc., to detect radar targets. Despite the many uses of these methods, they have not yet been able to meet the challenges of radar.  Therefore, in this paper, we have identified radar targets using the GMDH Deep Learning Algorithm. By simulating the proposed method and comparing it with other methods such as RIN, SAE, SCAE, SDAE, CNN, LSVM, K-SVD, the average has improved by 5%.

Keywords


[1]     Reddy, A. V., & Borkar, V. G. (2020). Design and Simulation of Microstrip Branch Line Coupler and Monopulse Comparator for Airborne Radar Applications. In Advances in Decision Sciences, Image Processing, Security and Computer Vision (pp. 10-18). Springer, Cham.##
[2]     Gallagher, K., Hedden, A., & Ranney, K. (2019, May). Issues associated with radar applications on software defined radios. In Radar Sensor Technology XXIII (Vol. 11003, p. 110031I). International Society for Optics and Photonics.##
[3]      ‏ Elfrgani, A., & Reddy, C. J. (2019, March). Near-Field RCS for Automotive Radar Applications. In 2019 International Workshop on Antenna Technology (iWAT) (pp. 217-220). IEEE.‏##
[4]     Luong, D., & Balaji, B. (2019, May). Radar applications of quantum squeezing. In Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII (Vol. 11018, p. 110181C). International Society for Optics and Photonics. ‏##
[5]      Schneider, D. A., Rösch, M., Tessmann, A., & Zwick, T. (2019). A Low-Loss W-Band Frequency-Scanning Antenna for Wideband Multichannel Radar Applications. IEEE Antennas and Wireless Propagation Letters, 18(4), 806-810.##
[6]      Bell, M. R., & Grubbs, R. A. (1993). JEM modeling and measurement for radar target identification. IEEE Transactions on Aerospace and Electronic Systems, 29(1), 73-87. ‏##
[7]      Martin, J., & Mulgrew, B. (1990, May). Analysis of the theoretical radar return signal form aircraft propeller blades. In IEEE International Conference on Radar (pp. 569-572). IEEE. ‏##
[8]      Cilliers, A., & Nel, W. A. J. (2008, September). Helicopter parameter extraction using joint time-frequency and tomographic techniques. In 2008 International Conference on Radar (pp. 598-603). IEEE. ‏##
[9]     Lim, H., & Myung, N. H. (2011). High resolution range profile-jet engine modulation analysis of aircraft models. Journal of Electromagnetic Waves and Applications, 25(8-9), 1092-1102. ‏##
[10]  Lee, J. H., & Kim, H. T. (2005). Radar target discrimination using transient response reconstruction. Journal of Electromagnetic Waves and Applications, 19(5), 655-669.##
[11]  Berni, A. J. (1975). Target identification by natural resonance estimation. IEEE Transactions on Aerospace and Electronic systems, (2), 147-154.##
[12]  Chuang, C. W., & Moffatt, D. L. (1976). Natural resonances of radar targets via Prony's method and target discrimination. IEEE Transactions on Aerospace and Electronic Systems, (5), 583-589.##
[13]  Aldhubaib, F., & Shuley, N. V. (2010). Radar target recognition based on modified characteristic polarization states. IEEE Transactions on Aerospace and Electronic Systems, 46(4), 1921-1933.##
[14]  Copeland, J. R. (1960). Radar target classification by polarization properties. Proceedings of the IRE, 48(7), 1290-1296.##
[15]  Xuesong, W., Shunping, X., Huamin, T., & Zhaowen, Z. (1997). Polarization radar target recognition based on curve fitting under the least square criterion using improved simulated annealing algorithm.##
[16]  Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning. Neural and Statistical Classification, 13.##
[17]  Watt, J., Borhani, R., & Katsaggelos, A. (2020). Machine learning refined: foundations, algorithms, and applications. Cambridge University Press.##
[18]  Buskirk, T. D. (2020). MACHINE LEARNING FOR SURVEY DATA. Presented at RTI International.##
[19]  Wang, P., Li, Y., & Reddy, C. K. (2019). Machine learning for survival analysis: A survey. ACM Computing Surveys (CSUR), 51(6), 110.##
[20]  Mane, D. T., & Kulkarni, U. V. (2020). A survey on supervised convolutional neural network and its major applications. In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 1058-1071). IGI Global. ##
[21]  Feng, B., Chen, B., & Liu, H. (2017). Radar HRRP target recognition with deep networks. Pattern Recognition, 61, 379-393.##
[22]  Karine, A., Toumi, A., Khenchaf, A., & El Hassouni, M. (2017). Target recognition in radar images using weighted statistical dictionary-based sparse representation. IEEE Geoscience and Remote Sensing Letters, 14(12), 2403-2407.##
[23]  Karine, A., Toumi, A., Khenchaf, A., & El Hassouni, M. (2018). Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation. Remote Sensing, 10(6), 843##
[24]  Yan, Y. (2018). Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition. Journal of Electronic Imaging, 27(2), 023024.##
[25]  Paulson, C., Wilson, J., & Lewis, T. (2018, April). Synthetic aperture radar quantized grayscale reference automatic target recognition algorithm. In Algorithms for Synthetic Aperture Radar Imagery XXV (Vol. 10647, p. 106470P). International Society for Optics and Photonics.##
[26]  Liu, S., & Yang, J. (2018). Target recognition in synthetic aperture radar images via joint multifeature decision fusion. Journal of Applied Remote Sensing, 12(1), 016012.##
[27]  Guo, C., He, Y., Wang, H., Jian, T., & Sun, S. (2019). Radar HRRP target recognition based on deep one-dimensional residual-inception network. IEEE Access, 7, 9191-9204.##
[28]  Long, T., Liang, Z., & Liu, Q. (2019). Advanced technology of high-resolution radar: target detection, tracking, imaging, and recognition. Science China Information Sciences, 62(4), 40301.##
[29]  Qi, B., Jing, H., Chen, H., Zhuang, Y., Yue, Z., & Wang, C. (2019). Target recognition in synthetic aperture radar image based on PCANet. The Journal of Engineering, 2019(21), 7309-7312.##
[30]   Farlow, S. J. (1981). The GMDH algorithm of Ivakhnenko. The American Statistician, 35(4), 210-215.##
[31]   Guo, C., He, Y., Wang, H., Jian, T., & Sun, S. (2019). Radar HRRP target recognition based on deep one-dimensional residual-inception network. IEEE Access, 7, 9191-9204.##
Volume 8, Issue 1 - Serial Number 23
September 2020
Pages 65-74
  • Receive Date: 07 January 2020
  • Revise Date: 15 August 2020
  • Accept Date: 24 August 2020
  • Publish Date: 22 August 2020