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Talati, S., Hasani Ahangar, M. (2021). Radar data processing using a combination of principal component analysis methods and self-organizing and digitized neural networks of the learning vector. Electronic and Cyber Defense, 9(2), 1-7.
S. Talati; M. R. Hasani Ahangar. "Radar data processing using a combination of principal component analysis methods and self-organizing and digitized neural networks of the learning vector". Electronic and Cyber Defense, 9, 2, 2021, 1-7.
Talati, S., Hasani Ahangar, M. (2021). 'Radar data processing using a combination of principal component analysis methods and self-organizing and digitized neural networks of the learning vector', Electronic and Cyber Defense, 9(2), pp. 1-7.
Talati, S., Hasani Ahangar, M. Radar data processing using a combination of principal component analysis methods and self-organizing and digitized neural networks of the learning vector. Electronic and Cyber Defense, 2021; 9(2): 1-7.

Radar data processing using a combination of principal component analysis methods and self-organizing and digitized neural networks of the learning vector

پدافند الکترونیکی و سایبری
Volume 9, Issue 2 - Serial Number 34, June 2021, Pages 1-7    PDF (783.03 K)
Document Type: Original Article
Authors
S. Talati1; M. R. Hasani Ahangar* 2
1Faculty of Electronic Warfare Engineering, Shahid Sattari University of aeronautical Science and Technology
2Associate Professor,Imam Hossein University
Receive Date: 24 September 2019,  Revise Date: 05 November 2020,  Accept Date: 25 January 2021 
Abstract
In military telecommunication systems, advanced techniques are used to intercept and process real-time signals that are critical to decisions related to electronic warfare and other tactical operations. Today, the need for intelligent systems with modern signal processing techniques is well felt. The main task of such systems is to identify the radars in the operating environment and classify them based on the previous learning of the system and perform the necessary operations at high speed and in real time, especially in cases where the received signal is related to an instantaneous threat such as missiles and electronic warfare systems. They may respond as a warning.The purpose of this study are to use the results of this research in classifying the information extracted by radar listening systems, which is achieved after the steps of selecting the input signal and selecting the correct classification algorithms, and another is to increase the speed using the vector vector digitization method. In this article, we present the data-driven methods of data collection using 4-digit vector learners and self-organizing methods.In this paper, we use learning vector quantization and self-organizing map methods to correlate the data. In this method, the neural network algorithm is first organized for the required coding positions, and in the next step, the quantization vector learning algorithm is created for data retrieval. In this article, we will also consider each database benchmark. The results obtained from the implementation of ordinary humanitarian command-and-control global standard deviation practices have been discussed in the light of the usual restraint methods, which demonstrate the great capability of these concepts.
Keywords
Radar Processing; PCA; LVQ; SOM
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[22]  S. Hashemi, M. Abyari, Sh. Barati, S. Tahmasebi, and S. Talati, “A proposed method to controller parameter soft tuning as accommodation FTC after unknown input observer FDI,” Journal of Engineering and Applied Sciences, vol. 11, pp. 2818-2829, 2016.##

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[25]  S. Talati, B. Ebadi, and H. Akbarzade, “Determining of the fault location in distribution systems in presence of distributed generation resources using the original post phasors,” QUID 2017, Special Issue No.1- ISSN: 1692-343X, Medellín-Colombia, pp. 1806-1812, April 2017.##

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[27]  S. Talati and P. Etezadifar, “Providing an Optimal Way to Increase the Security of Data Transfer Using Watermarking in Digital Audio Signals,” MJTD, vol. 10, no. 1, 2020.##

[28]  S. Talati and M. R. Hasani Ahangar, “Combining Principal Component Analysis Methods and Self-Organized and Vector Learning Neural Networks for Radar Data,” Majlesi Journal of Telecommunication Devices, vol. 9(2), pp. 65-69, 2020.##

[29]  M. R. Hasani Ahangar, S. Talati, A. Rahmati, and H. Heidari, “The Use of Electronic Warfare and Information Signaling in Network-based Warfare,” Majlesi Journal of Telecommunication Devices, vol. 9(2), pp. 93-97, 2020.##

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

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

[33]  S. Talati and S. M. Alavi, “Radar Systems Deception using Cross-eye Technique,” Majlesi Journal of Mechatronic Systems, vol. 9(3), pp. 19-21, 2020.##

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