Probability distribution of low‐altitude propagation loss from radar sea clutter data

Abstract

[1] This paper describes the estimation of propagation loss and its statistical properties on the basis of observations of radar sea clutter data. This problem is solved by first finding an ensemble of relevant refractivity model parameters, and each refractivity model is weighted according to its data likelihood function. A parabolic equation propagation model is used both in mapping from environmental model to radar clutter data and also when mapping to propagation loss. Two different methods are then used for mapping from a statistical description of refractivity parameters to a statistical description of propagation loss. In the first approach, all of the sampled models explored in the inversion are used to give a statistical description of propagation loss. Alternatively, the environmental model is sampled from the probability for the refractivity model parameters and then mapped into propagation loss. This can be done efficiently if we are using the one-dimensional marginal distributions instead of the full distribution for the environmental parameters.

Estimation of Radio Refractivity From Radar Clutter Using Bayesian Monte Carlo Analysis

“…2. The first attempt in estimating the M-profile from radar clutter returns using a maximum likelihood (ML) approach was made in [1] and was followed by similar studies, which used either a marching-algorithm approach [2] or the global-parametrization approach [3], [4]. The latter is adopted in this paper.…”

A Four-Parameter M-Profile Model for the Evaporation Duct Estimation From Radar Clutter

“…1) Using only the evaporation duct height to describe the actual surface layer environment is a limitation. Note that the M-deficit (the difference between the minimum value of modified refractivity and its value at the sea surface) of the trapping layer and the duct height are one-to-one correspondence in (2), i.e., the M-deficit is fixed for a certain duct height, which is unrealistic for many cases (e.g., the experimental profiles shown in Figure 1 by Goldhirsh and Dockery [13] and Figure 4 by Gerstoft et al [14]). …”

Bayesian Inversion of Radar Clutter

“…The results of this reseach are documented in five refered jornal papers [1][2][3][4][5][6] and one conference proceeding [7].…”

“…We have concentrated on demonstrating the feasibility of RFC using an efficient 11-parameter description of the environment. The quality of the inversion was addressed by comparing the field using the estimated parameters to a measured field [1][2][3][4][5][6]. Little has been done to indicate the quality of the solution for each parameter, either with the variance of parameter-estimate or preferably the complete a posteriori distribution.…”