Statistical Analysis of SAL Model-Based Atmospheric Phase Correction Algorithm (Preprint)
Abstract
Synthetic Aperture Ladar (SAL) is an emerging ladar remote sensing technology based on the well-established synthetic aperture sensing techniques, such as Synthetic Aperture Radar (SAR). A SAL sensor operates at optical instead of RF wavelengths. A key benefit of the reduction in wavelength is SAL sensors collect phase history data with an equivalent resolution to SAR in 10,000 shorter time. A key technical challenge limiting the efficacy of a SAL sensor is atmospheric turbulence. Advanced algorithms to mitigate atmospheric phase errors in measured SAL data are necessary to obtain the desired interpretable imagery when the atmosphere is the limiting factor in performance. In this paper, we conduct statistical performance analysis of a recently proposed algorithm known as the model-based atmospheric phase correction (MBAPC) and validate it using Monte Carlo simulations. Specifically, we derive the Cramer-Rao Lower Bound (CRLB) for the estimate ofthe unknown atmospheric model parameter. We show that the MBAPC algorithm asymptotically attains the CRLB as it is the maximum-likelihood estimator (MLE) under the assumption of additive complex white Gaussian noise (CWGN).
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2021
- Accession Number
- AD1140046
Entities
People
- Arnab K. Shaw
- Randy S Jr Depoy