showing 3 related works from this author
Classification of Targets Using Statistical Features from Range FFT of mmWave FMCW Radars
2021
Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including p…
A Self-Powered Long-range Wireless IoT Device based on LoRaWAN
2020
In this article, we propose a self-powered long-range wireless Internet-of-Things (IoT) device based on Long Range Wide Area Network (LoRaWAN) with various sensing capabilities. The nodes are designed based on ambient energy harvesting in such a way that these are self-sustainable throughout the components’ lifespan. Also, these nodes can be deployed on a large scale and are maintenance-free. In addition, these nodes can be deployed in remote places where the accessibility is limited, and maintenance is difficult. The wireless sensor nodes can be deployed both in indoor and outdoor environments with sufficient light levels for the solar panel, such as indoor lights in the indoor environment…
Object Classification Technique for mmWave FMCW Radars using Range-FFT Features
2021
In this article, we present a novel target classification technique by mmWave frequency modulated continuous wave (FMCW) Radars using the Machine Learning on raw data features obtained from range fast Fourier transform (FFT) plot. FFT plots are extracted from the measured raw data obtained with a Radar operating in the frequency range of 77- 81 GHz. The features such as peak, width, area, standard deviation, and range on range FFT plot peaks are extracted and fed to a machine learning model. Two light weight classification models such as Logistic Regression, Naive Bayes are explored to assess the performance. Based on the results, we demonstrate and achieve an accuracy of 86.9% using Logist…