Combining Principal Component Analysis Methods and Self-Organized and Vector Learning Neural Networks for Radar Data

Subject Areas : Majlesi Journal of Telecommunication Devices

Saeed  Talati ¹ (Department of Electronic Warfare Engineering, Shahid Sattari University of aeronautical Science and Technology, Tehran, Iran.)
Mohammad Reza  Hassani Ahangar ² (Department of Electronic Engineering, Imam Hossein University, Tehran, Iran.)

Received: 2020-05-13 Accepted : 2020-05-14 Published : 2020-06-01

Keywords: PCA, LVQ, radar, SOM,

Abstract :

The primary task of systems with real-time signal processing is to identify radars in the operating environment and to classify them based on prior system learning and to perform high-speed, real-time operations, especially where the received signal is an immediate threat such as missiles and requires war systems. Electronics should respond as soon as possible as an alarm. The purpose of this study is to use the results of this research to classify the information extracted by radar interception systems, which is achieved after the input signal selection stage and the correct selection of classification algorithms, and the other is accelerated by the learning vector quantization method. In this paper, we have presented a numerical method called a learning vector quantization, a method for data retrieval. In this method, the neural network algorithms are first organized to generate the required coding, and in the next step the digital vector learning algorithms will be created to retrieve the data. In this article, we will also consider each database benchmark. The implications of the usual implementation of universal command and control practices and their use of conventional restraint methods are a clear indication.

References:

[1] K. Hesampour, "Intelligent Selection of Optimum Properties and Separators for Automatic Signal Modulation Detection", Yazd University, 2011

[2] Hashemi, Seyed Mohammad, Shahrokh Barati, Saeed Talati and Heshmat Noori. “A GENETIC ALGORITHM APPROACH TO OPTIMAL PLACEMENT OF SWITCHING AND PROTECTIVE EQUIPMENT ON A DISTRIBUTION NETWORK.” VOL. 11, NO. 3, FEBRUARY 2016.

[3] O. Sharifi-Tehrani and S. Talati, “PPU Adaptive LMS Algorithm, a Hardware-Efficient Approach; a Review on”, Majlesi Journal of Mechatronic Systems, vol. 6, no. 1, Jun. 2017.

[4] Hashemi. Seyed Mohammad, Abyari. Mohammad, Barati. Shahrokh, Tahmasebi Sanaz, Talati, Saeed. “A PROPOSED METHOD TO CONTROLLER PARAMETER SOFT TUNING AS ACCOMMODATION FTC AFTER UNKNOWN INPUT OBSERVER.” ARPN Journal of Engineering and Applied Sciences VOL. 11, NO. 5, MARCH 2016.

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

[6] Saeed. Talati, Behzad. Ebadi, Houman. Akbarzade “Determining pf 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, Medellin-Colombia. April 2017

[7] Saeed Talati, Mohamadreza HasaniAhangar, “Analysis, Simulation and Optimization of LVQ Neural Network Algorithm and Comparison with SOM”, MJTD, vol. 10, no. 1, Jan. 2020.

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

[9] Saadati Moghaddam, Goodarz; Ali Naseri and Sayyed Hassanollah Asadollahi, Intelligent Algorithm for Identifying Radar Signals Using Matrix Multiplication and RBF Neural Network, 13th Iranian Student Electrical Engineering Conference, Tarbiat Modares University, Tehran,2010.

[10] Fathi, Abdolhossein and Shima Shafiei, An Approach to Machine Learning Algorithms for Artificial Neural Network, MLP Neural Network, RBF Neural Network, Third National Conference on Electrical and Computer Engineering Technology, Tehran, Payam Noor University and Payam Noor University of Tehran,2016

[11] Ghasemi, Mohammad Reza and Masoud Dadgar, LVQ Presentation of a Method for Algorithmic Classification of Data, 2nd Iranian National Congress on New Technologies for Sustainable Development, Tehran, Mehr Arvand Institute of Sustainable Development,2014

[12] J. A. Anderson, M. T. Gately, P. A. Penz, and D. R. Collins, “Radar signal categorization using a neural network,” Proceedings of the IEEE, vol. 78, no. 10, pp. 1646–1657, 1990.

[13] A. M. Aziz, “A novel and efficient approach for automatic classification of radar emitter signals,” in 2013 IEEE Aerospace Conference, 2013, pp. 1–8.

[14] B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92, 1992, pp. 144–152.

[15] M. Cherniakov, R. S. A. R. Abdullah, P. Jancovic, M. Salous, and V. Chapursky, “Automatic ground target classification using forward scattering radar,” Radar, Sonar and Navigation, IEE Proceedings, vol. 153, no. 5, pp. 427–437, Oct. 2006.

[16] C. L. Davies and P. Hollands, “Automatic processing for ESM,” IEE Proceedings F Communications, Radar and Signal Processing, vol. 129, no. 3, p. 164, Jun. 1982.

[17] J. Dudczyk, A. Kawalec, and J. Cyrek, “Applying the distance and similarity functions to radar signals identification,” in 2008 International Radar Symposium, 2008, pp. 1–4.

[18] P. M. Grant and J. H. Collins, “Introduction to electronic warfare,” IEE Proceedings F Communications, Radar and Signal Processing, vol. 129, no. 3, p. 113, Jun. 1982.