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RESEARCH PRODUCT

A Low Cost Solution for NOAA Remote Sensing

Alessandro BrunoRoberto PirroneFrancesco GugliuzzaEdoardo Ardizzone

subject

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSignal processingComputer Networks and CommunicationsComputer science0211 other engineering and technologiesComputer Science Applications1707 Computer Vision and Pattern RecognitionInformation System02 engineering and technologyRemote SensingRemote sensing (archaeology)Signal ProcessingInformation systemCommunications satelliteSatellite CommunicationElectrical and Electronic Engineering021101 geological & geomatics engineeringRemote sensing

description

United States National Oceanic and Atmospheric Administration (NOAA) weather satellites adopt Advanced Very High Resolution Radiometer (AVHRR) sensors to acquire remote sensing data and broadcast Automatic Picture Transmission (APT) images. The orientation of the scan lines is perpendicular to the orbit of the satellite. In this paper we propose a new low cost solution for NOAA remote sensing. More in detail, our method focuses on the possibility of directly sampling the modulated signal and processing it entirely in software enabled by recent breakthroughs on Software Defined Radios (SDR) and CPU computational speed, while keeping the costs extremely low. We aim to achieve good results with inexpensive SDR hardware, like the RTL-SDR (a repurposed DVB-T USB dongle). Nevertheless, we faced some problems caused by hardware limits such as high receiver noise figure and low ADC resolution. Furthermore, we detected several inherent drawbacks of frequent tuner saturations. For this purpose we developed a software-hardware integrated system able to perform the following steps: satellite pass prediction, time scheduling, signal demodulation, image cropping and filtering. Although we employed low cost components, we obtained good results in terms of signal demodulation, synchronization and image reconstruction.

yearjournalcountryeditionlanguage
2018-01-01Proceedings of the 7th International Conference on Sensor Networks
https://doi.org/10.5220/0006639101280134