AI-Based Optimization of Radar Signal Processing
Keywords:
Neural networks, TensorFlow, Keras, Radar signalsAbstract
Military vehicles often emit radar signals to ascertain their environment. Utilizing an Electromagnetic Support Measures receiver, they may be noticed, and it is vital to categorize them to ascertain the vehicle and radar kind. Despite the existence of several approaches for this task, there is a keen interest in automating and expediting the classification process to the greatest extent feasible. Artificial Neural Networks are a machine learning paradigm that has shown efficacy in categorizing sequential data from diverse sources. This thesis aims to examine the efficacy of Artificial Neural Networks in classifying various kinds of radar signals based on sequentially presented arrival times of radar pulses. Various forms of feed-forward and recurrent neural networks are examined, and strategies for their application to specific radar data are established. The findings indicate that artificial neural networks can categorize radar signals of this kind with an accuracy of up to 98 percent. Moreover, this might even be accomplished with little a few seconds of data with quite simple models.
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References
W. A. Gardner and C. M. Spooner, “Signal interception: Performance advantages of cyclic-feature detectors,” IEEE Transactions on Communications, vol. 40, no. 1,pp. 149–159, 1992.
C. M. Spooner and W. A. Gardner, “Robust feature detection for signal interception,” IEEE transactions on communications, vol. 42, no. 5, pp. 2165–2173, 1994.
A. Goldsmith, Wireless communications. Cambridge university press, 2005.
K. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.
C. Wang, J. Wang and X. Zhang, "Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 24372441.
B. Zhao, S. Xiao, H. Lu and J. Liu, "Waveforms classification based on convolutional neural networks," 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, 2017, pp. 162-165.
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