Generative adversarial networks (GANs) have recently been proposed for the synthesis of
RF micro-Doppler signatures to address the issue of low sample support and enable the …

Recently, deep neural networks (DNNs) have been the subject of intense research for the
classification of radio frequency signals, such as synthetic aperture radar imagery or micro …

Until recently, human surveillance has primarily been accomplished using video cameras.
However, radar offers unique advantages over optical sensors, such as being able to …

A key challenge for radar surveillance systems is the discrimination of ground‐based
targets, especially humans from animals, as well as different types of human activities. For …

Micro-Doppler signatures can be used not only to recognize different targets, such as
vehicles, helicopters, animals, and people, but also to classify varying activities, eg, walking …

Mel-frequency cepstrum coefficients (MFCC) have been used in many recent works as
features for micro-Doppler classification. Originally proposed as features for speech …

With the advent of new technology in wide-area motion imagery (WAMI) and full-motion
video (FMV), there is a capability to exploit the imagery in conjunction with other information …

Extracting biometric characteristics using radar requires a detailed understanding of the RF
scattering phenomenology associated with humans. The gross translational Doppler signals …

This chapter provides an overview on the modeling and simulation of human body motions
to observe their micro-Doppler effect in radar and extract micro-Doppler signatures. It …

Abstract Despite advances in Artificial Intelligence and Machine Learning (AI/ML) for
automatic target recognition (ATR) using surveillance radar, there remain significant …