Indoor person identification using a low-power FMCW radar

Contemporary surveillance systems mainly use video cameras as their primary sensor. However, video cameras possess fundamental deficiencies, such as the inability to handle low-light environments, poor weather conditions, and concealing clothing. In contrast, radar devices are able to sense in pitchdark environments and to see through walls. In this paper, we investigate the use of micro-Doppler (MD) signatures retrieved from a low-power radar device to identify a set of persons based on their gait characteristics. To that end, we propose a robust feature learning approach based on deep convolutional neural networks. Given that we aim at providing a solution for a real-world problem, people are allowed to walk around freely in two different rooms. In this setting, the IDentification with Radar data data set is constructed and published, consisting of 150 min of annotated MD data equally spread over five targets. Through experiments, we investigate the effectiveness of both the Doppler and time dimension, showing that our approach achieves a classification error rate of 24.70% on the validation set and 21.54% on the test set for the five targets used. When experimenting with larger time windows, we are able to further lower the error rate

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust

Applied optics research at imec : introduction to the feature issue

This feature issue provides an overview of the current applied optics research activities taking place at imec, Interuniversity Microelectronics Center, at its campuses in Leuven, Brussels and Ghent, Belgium. The issue contains articles covering wide range of topics on imaging systems, image processing, new materials, optical devices, sensors and detectors

A Cramer-Rao lower bound for analyzing the localization performance of a multistatic joint radar-communication system

It is expected that the 6G standards will have localization and sensing capabilities integrated as a part of the core technology. To that end, this paper considers the problem of localizing a point scatterer by using a distributed system of OFDM multi-antenna access points. Our setup envisions a joint hardware for both communications and sensing, where the communications waveform is reused for localization purposes. A multistatic approach is considered, and the Cramer-Rao lower bound is derived in order to assess the localization performance. The results are demonstrated by simulation and in an experimental setup, where it is observed that the bound is optimistic, but still within reasonable limits to the experimental results

On the Generalization and Reliability of Single Radar-Based Human Activity Recognition

Identifying human activities using short-range and low-power radars has attracted much attention among the researchers and consumer electronics industry. This paper considers human activity recognition in the context of a single Frequency Modulated Continuous Wave (FMCW) radar as the measurement tool. A classification pipeline is proposed to handle the data pre-processing and feature extraction and a machine-learning based solution is devised to undertake the activity classification. The performance of the proposed architecture is evaluated under both unseen subjects and new room layouts. We show how the accuracy of the activity classification will be affected by situations such as poor aspect-angle and occlusions created by furniture that normally arise in realistic scenarios where an unseen layout is considered. A two-stage classifier will be then proposed to enhance the generalization of the model, especially, to unseen rooms. Besides, an extensive feature exploration will be conducted and the importance of features in the generalization will be studied. The results in this paper will conclude a machine learning pipeline that will generalize well to unseen subjects and new room layouts, which are two main difficulties that arise in most radar-based activity classification tasks

Preamble-based frequency-domain joint CFO and STO estimation for OQAM-based filter bank multicarrier

status: publishe

Non-iterative method for finding optimised switching sequence to compensate gradient errors in current-steered DAC

© 2015 The Institution of Engineering and Technology. A methodology to find the optimised switching sequence for gradient error compensation in the current cell array of current-steered digitaltoanalogue converters (DACs) is presented. This new approach is simplified to be non-iterative and generalised to both linear and quadratic gradient errors. Simulation results show that the approach finds the optimised switching sequence to substantially reduce the nonlinearity of DACs due to the gradient errors.status: publishe

Adaptive Filter Design for Simultaneous In-Band Full-Duplex Communication and Radar

status: accepte