Further chapters focus on specific radar applications, which relate to DNN design for micro-Doppler analysis, SAR-based automatic target recognition, radar remote sensing, and emerging fields, such as data fusion and image reconstruction. Subsequently, the book summarizes radar-specific issues relating to the different domain representations in which radar data may be presented to DNNs and synthetic data generation for training dataset augmentation. The book begins with three introductory chapters on radar systems and phenomenology, machine learning principles, and optimization for training common deep neural network (DNN) architectures. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of. Zhu Bin, Jin Wei-dong (2012) Radar emitter signal recognition based on EMD and neural network.Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. In: CIE’06 international conference on radar Yuan X, Huang G, Zhang Q (2006) Implementation of radar emitter intelligent recognition system based on neural network. Wu Z, Yang Z, Yin Z, Zuo L, Gao H (2012) A novel RBF neural network for radar emitter recognition based on rough sets. Wiley RG (1982) ELINT, the interception and analysis of radar signals. Cambridge University Press, New York, pp 109–130 Spanos A (1999) Probability theory and statistical inference. Shieh CS, Lin CT (2002) A vector neural network for emitter identification. Sato A, Huang R, Yen NY (2015) Design of fusion technique-based mining engine for smart business. Pincus SM, Gladstone IM, Ehrenkranz RA (1991) A regularity statistic for medical data analysis. Petrov N, Jordanov IN, Roe J (2013) Radar emitter signals recognition and classification with feedforward networks. Lin CM, Chen YM (2014) A self-organizing interval type-2 fuzzy neural network for radar emitter identification. In: Third annual conference on advances in cognitive systems Lee-Urban S, Trewhitt E, Bieder I, Odom J, Boone T, Whitaker E (2015) CORA: A flexible hybrid approach to building cognitive systems. Keegan N, Ji S-Y, Chaudhary A, Concolato C, Yu B (2016) A survey of cloud-based network intrusion detection analysis. Granger E, Rubin MA, Grossberg S, Lavoie P (2001) A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. In: MILCOMĬho YS, Moon SC (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FART analysis. In: International conference on MICROWAVEĪrik M, Akan OB (2015) Enabling cognition on electronic counter measure systems against next-generation radars. Anjaneyulu L, Murthy NS, Sarma N (2008) Radar emitter classification using self-organising neural network models.
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