Comparison of analysis methods for separating and recognizing multicomponent radar signals
16 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.FEE.2024.120-127Keywords:
Radar signals; Multiresolution analysis; Maximal overlap discrete wavelet packet transform details; Empirical mode decomposition; Variational mode decomposition.Abstract
This paper proposes a model for separating and recognizing a mixture of radar signals using combined multiresolution methods and convolution neural networks. The model involves three main steps: separating the signal into individual components using the Multiresolution analysis (MRA) methods: Empirical mode decomposition (EMD), Variational mode decomposition (VMD), and Maximal overlap discrete wavelet packet transform (MODWPT); transforming these components into the time-frequency domain using Wigner-Ville distribution (WVD) and storing them as images; and then feeding these images into the SqueezeNet for recognition. These multiresolution methods are then compared based on three criteria: The number of successful separations, the SNR ratio of the input signal, and the correlation between the separated signal components and the original signal components. Additionally, we evaluate the performance of the SqueezeNet with real-time signals.
References
[1]. V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, and I. Maglogiannis, Eds., “Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4–7, 2018, Proceedings, Part I”, vol. 11139. Springer, pp 13-23, (2018). DOI: https://doi.org/10.1007/978-3-030-01418-6
[2]. Gers, F. A, “Long Short-Term Memory in Recurrent Neural Networks” These N° 2366, Présentée au Département d’Informatique, École Polytechnique Fédérale de Lausanne, pour l’obtention du grade de Docteur ès Sciences, (2001).
[3]. Norden E Huang, Samuel S P Shen, “Hilbert–Huang Transform and Its Applications”, 2 ed, World Scientific, pp 4-10, (2014).
[4]. Dragomiretskiy, K., & Zosso, D, “Variational Mode Decomposition”, IEEE Transactions on Signal Processing, 62 (3), pp 534-535, (2014). DOI: https://doi.org/10.1109/TSP.2013.2288675
[5]. Percival, D. B., & Walden, A. T, “Wavelet methods for time series analysis”, Cambridge University Press, pp. 6-7, (2000). DOI: https://doi.org/10.1017/CBO9780511841040
[6]. Katkovnik, V., and Stankovic, L., “Instantaneous frequency estimation using the Wigner distribution with varying and data-driven window length,” IEEE Trans. on Signal Processing, Vol. 46, No. 9, pp. 2315-2325, (1998). DOI: https://doi.org/10.1109/78.709514
[7]. Pham Thanh Cong, Le Dinh Phu Cuong, Vien Thanh Nha, Tiep Sy Minh Phung, Nguyen Thanh Tung, “Convolution neuron network (CNN) and VGG16 network on real-time face recognition system”, Journal of Science and Technology, 19(2), 1185-1196, (2022).
