Deep Learning-based identification of human gait by radar micro-Doppler measurements

For the first time identification of human individuals using micro-Doppler (m-D) features measured at X-band has been demonstrated. Deep Convolutional Neural Networks (DCNNs) have been used to perform classification. Inspection and visualization of the classification results were performed using Uniform Manifold Approximation and Projection ...

Chebychev moments based Drone Classification, Recognition and Fingerprinting

This paper introduces the use of a Chebychev moments' based feature for micro-Doppler based Classification, Recognition and Fingerprinting of Drones. This specific feature has been selected for its low computational cost and orthogonality property. The capability of the proposed feature extraction framework is assessed at three different levels...

Use of Symmetrical Peak Extraction in Drone Micro-Doppler Classification for Staring Radar

The commercialization of drones has granted the public with unprecedented access to unmanned aviation. As such, the detection, tracking, and classification of drones in radars have become an area in high demand to mitigate accidental or voluntary misuse of these platforms. This paper focuses on the classification of drone targets in a safety...

Derivative Target Line (DTL) for Continuous Human Activity Detection and Recognition

In this paper, we investigate the classification of Activities of Daily Living (ADL) by using a pulsed ultra-wideband radar. Specifically, we focus on contiguous activities that can be inseparable in time and share a common transition, such as walking and falling. The range-time data domain is deliberately exploited to determine transitions from...