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A Hardware Implemented Autocorrelation Technique for Estimating Power Spectral Density for Processing Signals from a Doppler Wind Lidar System

A signal processing technique utilizing autocorrelation of backscattered signals was designed and implemented in a 1.5 µm all-fiber wind sensing Coherent Doppler Lidar (CDL) system to preprocess atmospheric signals. The signal processing algorithm’s design and implementation are presented. The syste...

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Detalles Bibliográficos
Autores principales: Abdelazim, Sameh, Santoro, David, Arend, Mark, Moshary, Fred, Ahmed, Sam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308451/
https://www.ncbi.nlm.nih.gov/pubmed/30486511
http://dx.doi.org/10.3390/s18124170
Descripción
Sumario:A signal processing technique utilizing autocorrelation of backscattered signals was designed and implemented in a 1.5 µm all-fiber wind sensing Coherent Doppler Lidar (CDL) system to preprocess atmospheric signals. The signal processing algorithm’s design and implementation are presented. The system employs a 20 kHz pulse repetition frequency (PRF) transmitter and samples the return signals at 400 MHz. The logic design of the autocorrelation algorithm was developed and programmed into a field programmable gate array (FPGA) located on a data acquisition board. The design generates and accumulates real time correlograms representing average autocorrelations of the Doppler shifted echo from a series of adjustable range gates. Accumulated correlograms are streamed to a host computer for subsequent processing to yield a line of sight wind velocity. Wind velocity estimates can be obtained under nominal aerosol loading and nominal atmospheric turbulence conditions for ranges up to 3 km.