Publications

Version: 1.0 Approved

LDO-ID 2018-09-13-01

Title Clustering Algorithm for a spaceborne Lightning Imager: design, trade-off and FPGA implementation

Abstract

In this paper, the development and implementation of the Clustering Algorithm (CA) developed for an instrument to detect lightning phenomena (Lightning Imager) is presented. The aim of the Lightning Imager (LI) instrument is to provide information relevant to the localization and the radiance of lightning events with respect to terrestrial systems. A CMOS sensor with 1170   1000 resolution and 1000 fps frame rate is used on the instrument, with this characteristics LI able to identify the smaller lightning in terms of dimension (minimum diameter 10 km) and temporal pulse duration (minimum 0,6ms). The amount of acquired data is very large but only a small subset of pixels contain useful lightning information, thus information clustering is fundamental for downlink data-rate reduction. Raw sensor data are processed by an Application-Specific Integrated Circuit (ASIC) in order to extract pixels coordinates belonging to lightning flashes, Detected Transients (DTs), which are then sent directly to the CA implemented in Field-Programmable Gate Array (FPGA). DTs must be processed as they come to the CA due to the high throughput requirement. The objective of the CA is to define rectangular windows that enclose more DTs in order to send more compact information to Earth. The design presented in this paper was carried out through the development of a high-level model in order to verify the algorithm functionality and compliance with performance requirements. A lightning event generator was also developed, to simulate the DTs coming from the ASIC part. The hardware (HW) architecture was conceived and a bit-true model was developed to evaluate the implementation loss. The target device for the LI CA is a Microsemi RTAX2000 FPGA.

Authors Nannipieri Pietro, Davalle Daniele, Nencioni Stefano, Lombardi Paolo, Fanucci Luca

Type Paper for Seminar/Symposium/Conference

Media IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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Anno 2018