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....

Multipath Exploitation for Human Activity Recognition using a Radar Network

In this study, the problem of multipath in radar sensor networks for human activity recognition (HAR) has been examined. Traditionally considered as a source of additional clutter, the multipath is being investigated for its potential to be exploited through the creation of virtual radar nodes. These virtual nodes are conceptualized to...

A Survey on Radar-Based Continuous Human Activity Recognition

Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more....

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...

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...

3-D Ego-Motion Estimation Using Multi-Channel FMCW Radar

The problem of estimating the 3D ego-motion velocity using multi-channel FMCW radar sensors has been studied. For the first time, the problem of ego-motion estimation is treated using radar raw signals. A robust algorithm using multi-channel FMCW radar sensors to instantly determine the complete 3D motion state of the ego-vehicle (i.e.,...

Grouped People Counting Using mm-wave FMCW MIMO Radar

The problem of radar-based counting of multiple individuals moving as a single group is addressed using an mm-wave multiple-input-multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar. This problem is challenging because the different individuals are closer to each other than the range/azimuth resolution, and their bulk...

Vehicular Motion-based DOA Estimation with a Limited Amount of Snapshots for Automotive MIMO Radar

The problem of high-resolution direction-of-arrival (DOA) estimation based on a limited amount of snapshots in automotive multiple-input multiple-output (MIMO) radar has been studied. The number of snapshots is restricted to minimize target spread/migration in range and/or Doppler domains. A computationally efficient approach for side-looking...

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

A novel framework to enhance the angular resolution of automotive radars is proposed. An approach to enlarge the antenna aperture using artificial neural networks is developed using a self-supervised learning scheme. Data from a high angular resolution radar, i.e., a radar with a large antenna aperture, is used to train a deep neural network...

Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures

Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for spatial–temporal pattern extraction is proposed. The 2-D CNNs (2D-CNNs) are first applied to the radar data to perform spatial feature extraction on the...

Distributed radar fusion and recurrent networks for classification of continuous human activities

Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated in this paper using a network of five spatially distributed pulsed Ultra-Wideband radars. While activities performed continuously and in unconstrained trajectories provide a more realistic and natural scenario for HAR, the network of radar sensors is proposed...

Angle-insensitive Human Motion and Posture Recognition Based on 4D imaging Radar and Deep Learning Classifiers

The need for technologies for Human Activity Recognition (HAR) in home environments is becoming more and more urgent because of the aging population worldwide. Radar-based HAR is typically using micro-Doppler signatures as one of the main data representations, in conjunction with classification algorithms often inspired from deep learning...

A Novel Approach to Unambiguous Doppler Beam sharpening for Forward-looking MIMO Radar

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the...

A Bayesian Network for the Classification of Human Motion as Observed by Distributed Radar

In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node. Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures...

Phase-based Classification for Arm Gesture and Gross-Motor Activities using Histogram of Oriented Gradients

Micro-Doppler spectrograms are a conventional data representation domain for movement recognition such as Human Activity Recognition (HAR) or gesture detection. However, they present the problem of time-frequency resolution trade-offs of Short-Time Fourier Transform (STFT), which may have limitations due to unambiguous Doppler frequency, and the...

Human Motion Recognition With Limited Radar Micro-Doppler Signatures

The performance of deep learning (DL) algorithms for radar-based human motion recognition (HMR) is hindered by the diversity and volume of the available training data. In this article, to tackle the issue of insufficient training data for HMR, we propose an instance-based transfer learning (ITL) method with limited radar micro-Doppler (MD)...