FPGA Signal Processing, Signal-to-Noise Characterization, and Range-Dependent Turbulence Effects in an All-Fiber Coherent Doppler Lidar for Urban Atmospheric Boundary-Layer Observations

An integrated signal-processing, performance-characterization, and atmospheric-measurement framework is presented for an all-fiber coherent Doppler lidar operating near 1.5 µm for wind sensing and atmospheric boundary-layer observations. The lidar uses pulsed heterodyne detection at a pulse repetition rate of 20 kHz, with atmospheric return signals sampled at 400 MS/s and preprocessed in real time using a field-programmable gate array (FPGA). Two embedded processing architectures are consolidated: direct range-gated fast Fourier transform (FFT) processing with accumulated power spectra, and digital in-phase/quadrature demodulation followed by down-sampling and autocorrelation accumulation. The autocorrelation approach preserves flexibility for subsequent range-dependent processing. Receiver performance is characterized by normalizing measured spectra to a reference local-oscillator shot-noise spectrum to compensate for the non-flat receiver response. For 10,000-pulse accumulation, the measured normalized noise fluctuation is approximately 0.015–0.0155, compared with an idealized shot-noise value near 0.014, and a normalized detection threshold of approximately 0.024–0.025 is used. Range-dependent coherent return is interpreted through heterodyne efficiency and loss of spatial coherence caused by distributed aerosol scattering and atmospheric refractive-index fluctuations. Range-corrected coherent-lidar measurements are compared with direct-detection lidar observations, while coordinated microwave-radiometer measurements provide thermodynamic context. The combined results show that FPGA processing, statistical receiver characterization, range-dependent coherence analysis, and multi-sensor observations form a unified framework for quantitative coherent Doppler lidar measurements of urban boundary-layer wind and turbulence.