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

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

[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 envi...

Posterior distributions of a statistic of propagation loss inferred from radar sea clutter

[1] The generation of a posteriori distribution of a statistic of propagation loss conditioned on observed radar sea clutter is described. The statistic u is the 20th percentile level of the two-way propagation loss at a 5-m height over the 10–60 km range interval. A discrete implementation of the Bayesian paradigm is employed. The forward mapping from the space of environmental parameters into...

modeling loss data by phase-type distribution

بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...

Scattering Physics of Multistatic Radar Sea Clutter

Recursive Bayesian electromagnetic refractivity estimation from radar sea clutter

[1] Estimation of the rangeand height-dependent index of refraction over the sea surface facilitates prediction of ducted microwave propagation loss. In this paper, refractivity estimation from radar clutter returns is performed using a Markov state space model for microwave propagation. Specifically, the parabolic approximation for numerical solution of the wave equation is used to formulate t...