NDLI: Estimation of the parameters of stochastic differential equations for sea clutter

Content Provider	IET Digital Library
Author	Roussel, Clement Julien Coatanhay, Arnaud Baussard, Alexandre
Abstract	When the sea surface is illuminated by a radar sufficiently far away and in the absence of a coherent target, the scattered electromagnetic signal, called ‘sea clutter’ is unpredictable and can be represented by a stochastic process. A model based on stochastic differential equations and consistent with previous statistical models (K distribution) has been proposed. It depends on three parameters: A , B , and α . To estimate them, maximum likelihood estimators for A and B , and an estimator based on the hypothesis of ergodicity for α are proposed in this study. The authors compare three expressions for the transition probabilities: the exact one, Euler's approximation, and Nowman's approximation. By regenerating the trajectories from the same Brownian increments, they can quantify the typical error made on the sea clutter from the typical error made on the estimated parameters. Though the exact transition probabilities minimise the error on the sea clutter, they show that an approximation such as Euler's is sufficient.
Starting Page	497
Ending Page	504
Page Count	8
ISSN	17518784
Volume Number	13
e-ISSN	17518792
Issue Number	Issue 4, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/13/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5445
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-10-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximation Theory Brownian Increments Brownian Motion Differential Equation (numerical Analysis) Estimated Parameter Euler Approximation Maximum Likelihood Estimation Maximum Likelihood Estimators Nowman Approximation Probability Radar Clutter Radar Theory Scattered Electromagnetic Signal Sea Clutter Sea Surface Statistical Analysis Statistical Model Statistics Stochastic Differential Equation Stochastic Linearised SCUC Stochastic Process
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Radar sea-clutter modelling using fractional generalised Pareto distribution

Maximum likelihood gradient-based iterative estimation for multivariable systems

Explicit bipercentile parameter estimation of compound-Gaussian clutter with inverse gamma distributed texture

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Iterative maximum likelihood estimation of the compound inverse Gaussian clutter parameters

Parameter estimation of CGIG clutter plus noise using constrained NIOME and MLE approaches

A Comparative Study of Sea Clutter Covariance Matrix Estimators

Optimum coherent detection in homogenous K-distributed clutter

Parametric inference for mixed models defined by stochastic differential equations

Estimation of the parameters of stochastic differential equations for sea clutter

Similar Documents

Radar sea-clutter modelling using fractional generalised Pareto distribution

Maximum likelihood gradient-based iterative estimation for multivariable systems

Explicit bipercentile parameter estimation of compound-Gaussian clutter with inverse gamma distributed texture

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Iterative maximum likelihood estimation of the compound inverse Gaussian clutter parameters

Parameter estimation of CGIG clutter plus noise using constrained NIOME and MLE approaches

A Comparative Study of Sea Clutter Covariance Matrix Estimators

Optimum coherent detection in homogenous K-distributed clutter

Parametric inference for mixed models defined by stochastic differential equations

Estimation of the parameters of stochastic differential equations for sea clutter