A Review of Automatic Classification of Drones Using Radar: Key Considerations, Performance Evaluation and Prospects

Automatic target classification or recognition is a critical capability in noncooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognizing targets, including miniature unmanned air systems or drones (i.e., small, mini, micro, and nano...

DeepEgo: Deep Instantaneous Ego-Motion Estimation Using Automotive Radar

The problem of instantaneous ego-motion estimation with mm-wave automotive radar is studied. DeepEgo, a deep learning-based method, is proposed for achieving robust and accurate ego-motion estimation. A hybrid approach that uses neural networks to extract complex features from input point clouds and applies weighted least squares (WLS) for...

Radar Perception for Autonomous Unmanned Aerial Vehicles: A Survey

The advent of consumer and industrial Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has opened business opportunities in many fields, including logistics, smart agriculture, inspection, surveillance, and construction. In addition, the autonomous operations of UAVs reduce risks by minimizing the time spent by human workers...

Human activity classification with radar signal processing and machine learning

As the number of older adults increases worldwide, new paradigms for indoor activity monitoring are required to keep people living at home independently longer. Radar-based human activity recognition has been identified as a sensing modality of choice because it is privacy-preserving and does not require end-users compliance or manipulation. In...