A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Maximum-likelihood estimation of surface and rain backscattering parameters
An algorithm is developed for estimating simultaneously the rain and surface backscattering parameters from measurements obtained by a spaceborne single-frequency radar at off-nadir incidence. The authors first construct a simple stochastic rain model from which the joint conditional probability density function for the backscattering parameters is derived. This probability density function is updated in range by incorporating the radar data collected at previous ranges. The retrieval algorithm uses this probability density function to estimate the rain parameters, and a minimum-likelihood approach to estimate the surface backscattering coefficient. The preliminary simulation results using the TRMM radar parameters show that the algorithm can estimate the rain attenuation coefficient to better than 25% and the surface backscattering coefficient to better than 2 dB when the rain is uniform in range. The retrieval accuracy can be further improved for non-uniform rain because of lesser deterministic ambiguities existed in the radar measurements.<>
Maximum-likelihood estimation of surface and rain backscattering parameters
An algorithm is developed for estimating simultaneously the rain and surface backscattering parameters from measurements obtained by a spaceborne single-frequency radar at off-nadir incidence. The authors first construct a simple stochastic rain model from which the joint conditional probability density function for the backscattering parameters is derived. This probability density function is updated in range by incorporating the radar data collected at previous ranges. The retrieval algorithm uses this probability density function to estimate the rain parameters, and a minimum-likelihood approach to estimate the surface backscattering coefficient. The preliminary simulation results using the TRMM radar parameters show that the algorithm can estimate the rain attenuation coefficient to better than 25% and the surface backscattering coefficient to better than 2 dB when the rain is uniform in range. The retrieval accuracy can be further improved for non-uniform rain because of lesser deterministic ambiguities existed in the radar measurements.<>
Maximum-likelihood estimation of surface and rain backscattering parameters
Haddad, Z.S. (author) / Im, E. (author)
1993-01-01
280297 byte
Conference paper
Electronic Resource
English
Atmosphere - Maximum-Likelihood Estimation of Specific Differential Phase and Attenuation in Rain
| Online Contents | 2003
Maximum Likelihood Estimation for Combined Travel Choice Model Parameters
| British Library Conference Proceedings | 1998
Maximum Likelihood Estimation for Combined Travel Choice Model Parameters
| British Library Online Contents | 1998
Maximum Likelihood Estimation of K Distribution Parameters for SAR Data
| Online Contents | 1993
Estimation of Parameters in Geotechnical Backanalysis -- I. Maximum Likelihood Approach
| Online Contents | 1996