Wind profiling by a coherent Doppler lidar system VALIDAR with a subspace decomposition approach

摘要

The current nonlinear algorithm of the coherent Doppler lidar system VALIDAR at NASA Langley Research Center estimates wind parameters such as Doppler shift, power, wind velocity and direction by locating the maximum power and its frequency from the periodogram of the stochastic lidar returns. Due to the nonlinear nature of the algorithm, mathematically tractable parametric approaches to improve the quality of wind parameter estimates may pose a very little influence on the estimates especially in low signal-to-noise-ratio (SNR) regime. This paper discusses an alternate approach to accurately estimate the nonlinear wind parameters while preventing ambiguity in decision-making process via the subspace decomposition of wind data. By exploring the orthogonality between noise and signal subspaces expanded by the eigenvectors corresponding to the eigenvalues representing each subspace, a single maximum power frequency is estimated while suppressing erroneous peaks that are always present with conventional Fourier-transform-based frequency spectra. The subspace decomposition approach is integrated into the data processing program of VALIDAR in order to study the impact of such an approach on wind profiling with VALIDAR.