Statistical Modeling of Polarimetric RCS of Road Surfaces for Scattering Simulation and Optimal Antenna Polarization Determination

Incoherent backscattering of mm-waves from natural rough surfaces is considered. A novel method is proposed to determine the statistical properties of surface scattering from range profile measurements. The method is based on modeling the road surface as a grid of uncorrelated scattering elements, described by normalized scattering matrices....

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...

Road Surface Conditions Identification via H α A Decomposition and Its Application to mm-Wave Automotive Radar

A novel approach based on the entropy-alpha-anisotropy decomposition, also known as the $H\alpha A$ decomposition, for the recognition of road surface conditions using automotive radar is presented. To apply the $H\alpha A$ decomposition to automotive radar data, a dedicated signal processing pipeline has been developed. To investigate its...

The Human Activity Radar Challenge: Benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Statistical Polarimetric RCS Model of an Asphalt Road Surface for mm-Wave Automotive Radar

A method for extracting fully polarimetric statistical properties of road surface radar cross sections is presented. This method is subsequently applied to extract radar cross section information from an asphalt road surface. Furthermore, an approach is introduced to synthesise the scattered signal of road surface returns as measured by a radar....

Continuous Human Activity Classification with Radar Point Clouds and Point Transformer Networks

Due to numerous benefits, radar is considered as an important sensor for human activity classification. The problem of classifying continuous sequences of activities of unconstrained duration has been studied in this work. To tackle this challenge, a radar data processing method utilizing point transformer networks has been proposed. The method...

Classification of Unmanned Aerial Vehicles (UAVs) Carrying Payloads with Polarimetric Radar

The ability of a fully polarimetric radar to discriminate between payloads carried by UAVs is demonstrated. A novel approach has been employed in the feature extraction algorithm, where features from individual and combined polarimetric channels are extracted for classification. Decision and ensemble fusions on the respective extracted features...

Radar-PointGNN: Graph Based Object Recognition for Unstructured Radar Point-cloud Data

Perception systems for autonomous vehicles are reliant on a comprehensive sensor suite to identify objects in the environment. While object recognition systems in the LiDAR and camera modalities are reaching maturity, recognition models on sparse radar point measurements have remained an open research challenge. An object recognition model is...

Sequential Human Gait Classification with Distributed Radar Sensor Fusion

This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those...