Spectrum estimation from randomly sampled velocity data [LDV]

The power spectral density of randomly sampled signals is studied with reference to fluid velocity measured by laser Doppler velocimetry. We propose a new method for spectral estimation of Poisson-sampled stochastic processes. Our approach is based on polygonal interpolation from the sampled process followed by resampling and the usual fast Fourier transform. This study emphasizes the merit of the polygonal hold versus the sample-and-hold (zero order) and shows that polygonal interpolation results in better accuracy, especially at high frequencies. For purposes of illustrations the sampled process is assumed to be either a Kolmogorov or a Von Karman process. Numerical simulations and experimental results are given and confirm our theoretical analysis