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

Article Type:
Research/Original Article (دارای رتبه معتبر)

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.

Journal of Electronic and Cyber Defense, Volume:9 Issue: 2, 2021
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