Deep Convolutional Neural Network driven Neuro-Fuzzy System for Moving Target Detection Using the Radar Signals

M. Bharat Kumar and P. Rajesh Kumar ()
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M. Bharat Kumar: Department of Electronics and Communication Engineering, A U College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh 530003, India
P. Rajesh Kumar: Department of Electronics and Communication Engineering, A U College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh 530003, India

Journal of Information & Knowledge Management (JIKM), 2022, vol. 21, issue 01, 1-24

Abstract: In radar signal processing, detecting the moving targets in a cluttered background remains a challenging task due to the moving out and entry of targets, which is highly unpredictable. In addition, detection of targets and estimation of the parameters have become a major constraint due to the lack of required information. However, the appropriate location of the targets cannot be detected using the existing techniques. To overcome such issues, this paper presents a developed Deep Convolutional Neural Network-enabled Neuro-Fuzzy System (Deep CNN-enabled Neuro-Fuzzy system) for detecting the moving targets using the radar signals. Initially, the received signal is presented to the Short-Time Fourier Transform (STFT), matched filter, radar signatures-enabled Deep Recurrent Neural Network (Deep RNN), and introduced deep CNN to locate the targets. The target location output results are integrated using the newly introduced neuro-fuzzy system to detect the moving targets effectively. The proposed deep CNN-based neuro-fuzzy system obtained effective moving target detection results by varying the number of targets, iterations, and the pulse repetition level for the metrics, like detection time, missed target rate, and MSE with the minimal values of 1.221s, 0.022, and 1,952.15.

Keywords: Moving target detection; deep recurrent neural network; deep convolutional neural network; radar signatures; short-time Fourier transform (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219649222500101

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