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Comparison between the Kalman and the non-linear least-squares estimators in low signal-to-noise ratio lidar inversion
Rocadenbosch Burillo, Francisco; Sicard, Michaël; Comerón Tejero, Adolfo; Md. Reba, Mohd Nadzri
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
This works departs from previously published results of the authors and focus on joint estimation and time evolution of the atmospheric backscatter profile and a range-independent lidar ratio by means of 1) adaptive extended Kalman filtering (EKF) and 2) non-linear least-squares (NLSQ), under moderate-to-low signal-to-noise ratios (SNR<100 at the starting sounding range). A Rayleigh/Mie atmosphere and a calibrated lidar system are considered. Performance parameters studied are data sufficiency, tracking of the optical parameter time fluctuations, inversion errors, power estimation, and noise impact. The EKF inversion solution is, in turn, compared with Klett's method as a reference. Finally, it is shown that the EKF outweighs the NSLQ in noisy environments.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
Remote sensing
Atmosphere -- Laser observations
Kalman filter
Lidar
Inversion
Least-squares
Teledetecció
Atmosfera -- Observacions amb làser
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
         

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