Distinction of Target and Chaff Signals by Suggesting the Optimal Waveform in Cognitive Radar using Artificial Neural Network

الموضوعات : Majlesi Journal of Telecommunication Devices

Seyed Mohammad Mahdi Ziaei ¹ ( PhD Candidate, Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran )
Pouriya Etezadifar ² ( Assistant Professor, Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran. )
Yaser Norouzi ³ ( Assistant Professor, Faculty of Electrical Engineering Department, Amirkabir University, Tehran, Iran )
Nadali Zarei ⁴ ( Assistant Professor, Faculty of Electrical Engineering Department, Imam Hossein University, Tehran, Iran )

تاريخ الإرسال : 28 الأحد , رجب, 1444 تاريخ التأكيد : 08 الجمعة , شوال, 1444 تاريخ الإصدار : 12 الخميس , ذو القعدة, 1444

الکلمات المفتاحية: Chaff, Target, radar, Waveform, Artificial Neural Network,

ملخص المقالة :

Using chaff to deflect missile guidance radar or missile seeker is a common and effective defense method in military vessels. To deal with this defensive method, focus on specific characteristics of the target and chaff signals. These features should be able to perform properly in different operating conditions of the radar or different environmental conditions that change the behavior of the radar’s return signals. But there is no feature that can distinguish the target from the target with appropriate accuracy in all conditions. In this article, a structure is presented for detecting chaff and target in a radar and has been able to improve the accuracy of target detection in presence of chaff. Also, to improve the performance of the radar with a cognitive approach, its transmitted waveform is optimally selected and changed at each stage. For this purpose, a feedback neural network with LSTM layers has been used. The general structure of the proposed method uses pre-processing on the received radar signals and extracts symmetry characteristics, Doppler spread and AGCD from it to contain the information for separating the target and chaff. Then, to remove the effect of noise on the features. Finally, these features are used to correctly distinguish the target from the chaff in a feed-forward neural network with fully connected layers. At the end, the effectiveness of this method is compared to the previous methods. It can be seen that the performance of the proposed system has made a significant improvement in accuracy of detection.

المصادر:

• Sherman, “Bistatic RCS of Spherical Chaff Clouds”, IEEE Transactions on Antennas and Propagation, 2015


• E, “A novel discrimination method of ship and chaff based on sparseness for naval radar”, IEEE 2022.


• Shang, B. X. Chen, and L .F. Jiang, “An anti-chaff jamming method based on the effect of spectral expansion,” Guidance & Fuze, Vol. 27, No. 3, 2006, pp. 5–10.


• H. Shao, H. Du, and J. H. Xue, “A target recognition method based on non-linear polarization transformation,” IEEE International Workshop on Anti-counterfeiting, Security, Identification, 2007, pp. 157–163.


• W. Fu; S. W. Zhang; and X. M. Li, “A recognition method of chaff jamming based on gray principle,” Electronics Optics & Control, Vol.10, No. 3, 2003, pp. 42- 44.


• E, Chaff Jamming Recognition for Anti-Vessel End-guidance Radars, IEEE.2022.


• Estes, “Spectral Characteristics of Radar Echoes from Aircraft dispensed chaff”, IEEE Trans AES, 1985,21(7) 8-19.


• E, “Symmetry Measurement of Radar Echos and its Aoolication in Ship and Chaff”, IEEE.2022.


• Charlish, S&T, “Anticipation in Cognitive Radar Using Stochastic Control”, Organization NATO, 2016.


• Sevgi Gurbuz,” An Overview of Cognitive Radar: Past, Present”, and Future, IEEE Aerospace and Electronic Systems Magazine · December 2019


• Wang et.al., “Chaff identification method based on Range‐Doppler imaging feature”, IET Radar, Sonar & Navigation, July 2022.


• Yongzhen Li et.al., “Ship Recognition from Chaff Clouds with Sophisticated Polarimetric Decomposition”, remote sensing MDPI, June 2020.


• ZHE GENG, “Deep-Learning for Radar: A Survey”, IEEE Access, October 2021.


• P., Talati. S., Hassani Ahangar. M.R., Molazade. M., “Investigation of Steganography Methods in Audio Standard Coders: LPC, CELP, MELP” Majlesi Journal of Telecommunication Devices, 12(1), in press, 2023.


• Etezadifar, P., & Talati, S. (2021). “Analysis and Investigation of Disturbance in Radar Systems Using New Techniques of Electronic Attack”.Majlesi Journal of Telecommunication Devices, 10(2), 55-59.


• Saeed Talati, Pouriya Etezadifar. (2020). “Providing an Optimal Way to Increase the Security of Data Transfer Using Watermarking in Digital Audio Signals”,MJTD, vol. 10, no. 1.


• Hashemi, Seyed & Barati, Shahrokh & Talati, S. & Noori, H. (2016). “A genetic algorithm approach to optimal placement of switching and protective equipment on a distribution network”. Journal of Engineering and Applied Sciences. 11. 1395-1400.


• Talati, A. Rahmati, and H. Heidari. (2019) “Investigating the Effect of Voltage Controlled Oscillator Delay on the Stability of Phase Lock Loops”,MJTD, vol. 8, no. 2, pp. 57-61.


• Talati, S., & Alavi, S. M. (2020). “Radar Systems Deception using Cross-eye Technique”.Majlesi Journal of Mechatronic Systems, 9(3), 19-21.


• Saeed Talati, mohamadreza Hasani Ahangar (2020) “Analysis, Simulation and Optimization of LVQ Neural Network Algorithm and Comparison with SOM”, MJTD, vol. 10, no. 1.


• Talati, S., & Hassani Ahangar. M. R. (2020) “Combining Principal Component Analysis Methods and Self-Organized and Vector Learning Neural Networks for Radar Data”,Majlesi Journal of Telecommunication Devices, 9(2), 65-69.


• Hassani Ahangar, M. R., Talati, S., Rahmati, A., & Heidari, H. (2020). “The Use of Electronic Warfare and Information Signaling in Network-based Warfare”. Majlesi Journal of Telecommunication Devices, 9(2), 93-97.


• Aslinezhad, M., Mahmoudi, O., & Talati, S. (2020). “Blind Detection of Channel Parameters Using Combination of the Gaussian Elimination and Interleaving”.Majlesi Journal of Mechatronic Systems, 9(4), 59-67.


• Talati, S., & Amjadi, A. (2020). “Design and Simulation of a Novel Photonic Crystal Fiber with a Low Dispersion Coefficient in the Terahertz Band”.Majlesi Journal of Mechatronic Systems, 9(2), 23-28.


• Talati, Saeed, Hassani Ahangar, Mohammad Reza. (2021). “Radar Data Processing Using a Combination of Principal Component Analysis Methods and Self-Organized and Digitizing Learning Vector Neural Networks”, Electronic and Cyber Defense, 9 (2), pp. 1-7.


• Talati, S., Alavi, S. M., & Akbarzade, H. (2021). “Investigating the Ambiguity of Ghosts in Radar and Examining the Diagnosis and Ways to Deal with it”. Majlesi Journal of Mechatronic Systems, 10(2).


• Talati, Behzad. Ebadi, Houman. Akbarzade “Determining of the fault location in distribution systems in presence of distributed generation resources using the original post phasors”. QUID 2017, pp. 1806-1812, Special Issue No.1- ISSN: 1692-343X, Medellín-Colombia. April 2017.


• Talati, Saeed, Akbari Thani, Milad, Hassani Ahangar, Mohammad Reza. (2020). “Detection of Radar Targets Using GMDH Deep Neural Network”, Radar Journal, 8 (1), pp. 65-74.


• Talati, S., Abdollahi, R., Soltaninia, V., & Ayat, M. (2021). “A New Emitter Localization Technique Using Airborne Direction Finder Sensor”.Majlesi Journal of Mechatronic Systems, 10(4), 5-16.