Deep Learning Applications of Short-range Radars

This exciting new resource covers various emerging applications of short range radars, including people counting and tracking, gesture sensing, human activity recognition, air-drawing, material classification, object classification, vital sensing by extracting features such as range-Doppler Images (RDI), range-cross range images, Doppler Spectrogram or directly feeding raw ADC data to the classifiers. The book also presents how deep learning architectures are replacing conventional radar signal processing pipelines enabling new applications and results. It describes how deep convolutional neural networks (DCNN), long-short term memory (LSTM), feedforward networks, regularization, optimization algorithms, connectionist temporal classification (CTC) are enabling these applications.
Readers gain in-depth knowledge of how deep learning is enabling solve emerging problems and also solving existing problems. New applications and problems in the field of radar are outlined. The book also provides transitions from conventional signal processing pipeline to machine/deep learning pipelines and explains how radars transition into industrial and consumer applications from aerospace and automotive applications.

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