[1]. Skolnik, M., Introduction to Radar Systems. 2002: McGraw-Hill Science/Engineering/Math.
[2]. Xia, Q.T., Verification of high resolution precipitation forecast by radar-based data. Signal Processing, 2015. 108: p. 159–166.
[3]. Zanoni, M., F.A. Fontana, and F. Stella On applying machine learning techniques for design pattern detection. Journal of Systems and Software, 2015. 103: p. 102–117.
[4]. Chen, P. and C.Y. Zhang, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 2014. 275: p. 314–347.
[5]. Gandomi, A. and M. Haider, Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 2015. 35(2): p. 137–144.
[6]. Rojo-Álvarez, J.L., et al., From Signal Processing to Machine Learning. 2018.
[7]. Bagwe, R., et al. Automated Radar Signal Analysis Based on Deep Learning. in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). 2020. IEEE.
[8]. Mahmoud, H.H.H. and T. Ismail. A Review of Machine learning Use-Cases in Telecommunication Industry in the 5G Era. in 2020 16th International Computer Engineering Conference (ICENCO). 2020. IEEE.
[9]. Huang, J.-C., et al., Application and comparison of several machine learning algorithms and their integration models in regression problems. Neural Computing and Applications, 2020. 32(10): p. 5461-5469.
[10]. Wang, L., J. Tang, and Q. Liao, A study on radar target detection based on deep neural networks. IEEE Sensors Letters, 2019. 3(3): p. 1-4.
[11]. Richards, M.A., Fundamentals of radar signal processing. 2014: McGraw-Hill Education.
[12]. Ding, J., et al., Convolutional neural network with data augmentation for SAR target recognition. IEEE Geoscience and remote sensing letters, 2016. 13(3): p. 364-368.
[13]. Liu, H.-w., et al., Radar high-resolution range profiles target recognition based on stable dictionary learning. IET Radar, Sonar & Navigation, 2016. 10(2): p. 228-237.
[14]. Li, H., W. Jing, and Y. Bai. Radar emitter recognition based on deep learning architecture. in 2016 CIE International Conference on Radar (RADAR). 2016. IEEE.
[15]. Kong, M., et al. Radar emitter identification based on deep convolutional neural network. in 2018 International Conference on Control, Automation and Information Sciences (ICCAIS). 2018. IEEE.
[16]. Cao, Y., et al., Deep representation method for radar emitter signal using wavelet packets decomposition. The Journal of Engineering, 2019. 2019(19): p. 6282-6286.
[17]. Kim, Y. and T. Moon, Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE geoscience and remote sensing letters, 2015. 13(1): p. 8-12.
[18]. Park, J., et al., Micro-Doppler based classification of human aquatic activities via transfer learning of convolutional neural networks. Sensors, 2016. 16(12): p. 1990.
[19]. Chen, S. and H. Wang. SAR target recognition based on deep learning. in 2014 International Conference on Data Science and Advanced Analytics (DSAA). 2014. IEEE.
[20]. Honeiné, P., et al. Optimal selection of time-frequency representations for signal classification: A kernel-target alignment approach. in 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings. 2006. IEEE.
[21]. Honeine, P. and C. Richard. Signal-dependent time-frequency representations for classification using a radially gaussian kernel and the alignment criterion. in 2007 IEEE/SP 14th Workshop on Statistical Signal Processing. 2007. IEEE.
[22]. Honeine, P., C. Richard, and P. Flandrin, Time-frequency learning machines. IEEE Transactions on Signal Processing, 2007. 55(7): p. 3930-3936.
[23]. Kederer, W. and J. Detlefsen. Direction of Arrival (DOA) determination based on monopulse concepts. in Asia-Pacific Microw.Conf. 2000. Sydney, Australia.
[24]. Kederer, W. and J. Detlefsen, Comparison of amplitude matching and complex monopulse algorithms with respect to SNR. AEU - International Journal of Electronics and Communications, 2003. 57(3): p. 68–172.
[25]. Li, J., G. Fan, and Q. Mei. A new method to find the direction of radar signal. 1996. Beijing, China: CIE Int. Conf. Radar.
[26]. Taillefer, E., A. Hirata, and T. Ohira, Direction-of-arrival estimation using radiation power pattern with an ESPAR antenna. IEEE Transaction, Antennas and Propagation, 2005. 53(2): p. 678 - 684.
[27]. Bracco, C., S. Marcos, and M. Benidir. Improving the resolution of a sensor array pattern by neural networks. 1994. Ermioni, Greece: IEEE Workshop on Neural Networks and Signal Processing.
[28]. Southall, H.L., J.A. Simmers, and T.H. O’Donnel, Direction finding in phased arrays with a neural network beamformer. IEEE Transactions on Antennas and Propagation, 1995. 43: p. 1369–1374.
[29]. Zhooghby, E., C.G. Christodoulou, and M. Giorgiopoulos. Performance of radial-basis function networks for direction of arrival estimation with antenna arrays. 1997. IEEE Transaction on Antennas and Propagation.
[30]. Tayem, N. and H.M. Kwon, L-shaped 2-dimensional arrival angle estimation with propagator method. Vehicular Technology Conference, 2005. 53: p. 1622–1630.
[31]. Gan, L., J.-F. Gu, and P. Wei, Estimation of 2-D DOA for noncircular sources using simultaneous SVD technique. Antennas and Wireless Propagation Letters, IEEE, 2008. 7: p. 385 - 388.
[32]. Zhang, T.T., Y.L. Lu, and H.T. Hui, Compensation for the mutual coupling effect in uniform circular arrays for 2D DOA estimations employing the maximum likelihood technique. IEEE Transactions on Aerospace and Electronic Systems, 2008. 44(3): p. 1215–1221.
[33]. Chan, F.K.W., et al., Underdetermineddirection-of-departureand direction-of-arrivalestimationinbistatic multiple-inputmultiple-outputradar. Signal Processing, 2014. 104: p. 284–290.
[34]. Himovich, A.H., R.S. Blum, and L.J. Cimini, MIMO radar with widely separated antennas. IEEE Signal Process .Mag., 2008. 1(50): p. 116–129.
[35]. Hassanien, A., S.A. Vorobyov, and A.B. Gershman, Moving target parameters estimation in noncoherent MIMO radar systems. IEEETrans. Signal Process, 2012. 5(60): p. 2354–2361.
[36]. Tang, B., et al., Maximum likelihood estimation of DOD and DOA for bistatic MIMO radar. Signa lProcess, 2013. 93(5): p. 1349–1357.
[37]. Rambach, K. and B. Yang. Colocated MIMO radar: Cramer-Rao bound and optimal time division multiplexing for DOA estimation of moving targets. 2013. Vancouver, BC: IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP).
[38]. Chandwani, V., V. Agrawal, and R. Nagar, Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks. Expert Systems with Applications, 2015. 2(42): p. 885–893.
[39]. Wang, Q., et al. Improved genetic neural network for image segmentation. 2011. 2011 IEEE 18th International Conference on Industrial 588.
[40]. Rivera-Borroto, O.M., et al., Dunn’s index for cluster tendency assessment of pharmacological data sets. Canadian Journal of Physiology and Pharmacology, 2012. 4(90): p. 425-433.
