Search results for "Radar"
showing 10 items of 248 documents
A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars
2021
Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow veloci…
Localization of Multi-Class On-Road and Aerial Targets Using mmWave FMCW Radar
2021
mmWave radars play a vital role in autonomous systems, such as unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), ground station control and monitoring systems. The challenging task when using mmWave radars is to estimate the accurate angle of arrival (AoA) of the targets, due to the limited number of receivers. In this paper, we present a novel AoA estimation technique, using mmWave FMCW radars operating in the frequency range 77–81 GHz by utilizing the mechanical rotation. Rotating the radar also increases the field of view in both azimuth and elevation. The proposed method estimates the AoA of the targets, using only a single transmitter and receiver. The measurements are…
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…
Explotación sinérgica de datos multiespectrales y radar para la estimación de variables biofísicas de la vegetación mediante tecnologías de sensorami…
2023
Las variables biofísicas de la vegetación (VBV) son indicadores directos del crecimiento y productividad de los cultivos. Los sistemas de observación de la Tierra (EO–Earth observation) presentan oportunidades sin precedentes para el monitoreo de las variables biofísicas del trigo. Sentinel–2 (S2) es una constelación de satélites que forma parte de las misiones Sentinel del programa Copernicus de EO. El período de revisita, así como su resolución espacial y espectral, han convertido a S2 en un sistema de EO trascendental para el monitoreo de VBV. Los sistemas ópticos de EO se ven limitados con frecuencia por las condiciones climáticas tales como nubosidad o precipitaciones. En este sentido,…
Šķēršļu konstatēšana uz dzelzceļa pārbrauktuves, izmantojot milimetru viļņu radaru sensorus
2022
Bakalaura darbā "Šķēršļu konstatēšana uz dzelzceļa pārbrauktuves, izmantojot milimetru viļņu radaru sensorus" tiek apskatīts milimetru viļņu radaru pielietojums dzelzceļa pārbrauktuvju novērošanas sistēmas prototipa izstrādē. Darbs tika izstrādāts Elektronikas un datorzinātņu institūta projekta ietvaros. Darba mērķis ir izpētīt dzelzceļa pārbrauktuvju drošības situāciju Latvijā, pieejamos drošības risinājumus un radaru darbību kā arī veikt milimetru viļņu radaru sensoru dzelzceļa pārbrauktuvju monitorēšanas sistēmas prototipa izstrādi.
Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
2023
Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Senti…
Nuove tecnologie radar per il monitoraggio delle deformazioni superficiali del terreno: casi di studio in Sicilia
2009
Sviluppo di un sistema di monitoraggio per lo studio delle dinamiche meteorologiche nell'area urbana di Palermo.'
2013
Sensitivity analysis of Gaussian processes for oceanic chlorophyll prediction
2015
Gaussian Process Regression (GPR) for machine learning has lately been successfully introduced for chlorophyll content mapping from remotely sensed data. The method provides a fast, stable and accurate prediction of biophysical parameters. However, since GPR is a non-linear kernel regression method, the relevance of the features are not accessible. In this paper, we introduce a probabilistic approach for feature sensitivity analysis (SA) of the GPR in order to reveal the relative importance of the features (bands) being used in the regression process. We evaluated the SA on GPR ocean chlorophyll content prediction. The method revealed the importance of the spectral bands, thus allowing the …