Abstract-The merit of evolutionary algorithms (EA) to solve convex optimization problems is widely acknowledged. In this paper, a genetic algorithm (GA) optimization based waveform design framework is used to improve the features of radar pulses relying on the orthogonal frequency division multiplexing (OFDM) structure. Our optimization techniques focus on finding optimal phase code sequences for the OFDM signal. Several optimality criteria are used since we consider two different radar processing solutions which call either for single or multipleobjective optimizations. When minimization of the so-called peak-to-mean envelope power ratio (PMEPR) single-objective is tackled, we compare our findings with existing methods and emphasize on the merit of our approach. In the scope of the two-objective optimization, we first address PMEPR and peakto-sidelobe level ratio (PSLR) and show that our approach based on the non-dominated sorting genetic algorithm-II (NSGA-II) provides design solutions with noticeable improvements as opposed to random sets of phase codes. We then look at another case of interest where the objective functions are two measures of the sidelobe level, namely PSLR and the integrated-sidelobe level ratio (ISLR) and propose to modify the NSGA-II to include a constrain on the PMEPR instead. In the last part, we illustrate via a case study how our encoding solution makes it possible to minimize the single objective PMEPR while enabling a target detection enhancement strategy, when the SNR metric would be chosen for the detection framework.
Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day Albedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a climate data record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: One for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition, that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.
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