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.
An eye-safe coherent Doppler Lidar (CDL) system for wind measurement was developed and tested at the Remote Sensing Laboratory of the City College of New York (CCNY). The system employs a 1542 nm fiber laser to leverage components' availability and affordability of the telecommunication industry. A balanced detector with a bandwidth extending from dc to 125 MHz is used to eliminate the common mode relative intensity noise (RIN). The system is shot noise limited i.e., the dominant component of received signals' noise is the shot noise. Wind velocity can be measured under nominal aerosol loading and atmospheric turbulence conditions for ranges up to 3 km while pointing vertically with 0.08 m/s precision.
A field deployable all-fiber eye-safe Coherent Doppler LIDAR is being developed at the Optical Remote Sensing Lab at the City College of New York (CCNY) and is designed to monitor wind fields autonomously and continuously in urban settings. Data acquisition is accomplished by sampling lidar return signals at 400 MHz and performing onboard processing using field programmable gate arrays (FPGAs). The FPGA is programmed to accumulate signal information that is used to calculate the power spectrum of the atmospherically back scattered signal. The advantage of using FPGA is that signal processing will be performed at the hardware level, reducing the load on the host computer and allowing for 100% return signal processing. An experimental setup measured wind speeds at ranges of up to 3 km.
In this paper, we present two signal processing algorithms implemented using the FPGA. The first algorithm involves explicate time gating of received signals that correspond to a desired spatial resolution, performing a Fast Fourier Transform (FFT) calculation on each individual time gate, taking the square modulus of the FFT to form a power spectrum and then accumulating these power spectra for 10k return signals. The second algorithm involves calculating the autocorrelation of the backscattered signals and then accumulating the autocorrelation for 10k pulses. Efficient implementation of each of these two signal processing algorithms on an FPGA is challenging because it requires there to be tradeoffs between retaining the full data word width, managing the amount of on chip memory used and respecting the constraints imposed by the data width of the FPGA. A description of the approach used to manage these tradeoffs for each of the two signal processing algorithms are presented and explained in this article. Results of atmospheric measurements obtained through these two embedded programming techniques are also presented.
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