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Ebrahim Zadeh, A., Akhondi, M. (2016). Parameter Estimation of K Distribution Radar Clutter with the Gravity Searching Algorithm. Electronic and Cyber Defense, 4(3), 55-65.
Ataollah Ebrahim Zadeh; Mohammad Akhondi. "Parameter Estimation of K Distribution Radar Clutter with the Gravity Searching Algorithm". Electronic and Cyber Defense, 4, 3, 2016, 55-65.
Ebrahim Zadeh, A., Akhondi, M. (2016). 'Parameter Estimation of K Distribution Radar Clutter with the Gravity Searching Algorithm', Electronic and Cyber Defense, 4(3), pp. 55-65.
Ebrahim Zadeh, A., Akhondi, M. Parameter Estimation of K Distribution Radar Clutter with the Gravity Searching Algorithm. Electronic and Cyber Defense, 2016; 4(3): 55-65.

Parameter Estimation of K Distribution Radar Clutter with the Gravity Searching Algorithm

رادار
Article 4, Volume 4, Issue 3, January 2016, Pages 55-65    PDF (1.05 M)
Document Type: Original Article
Authors
Ataollah Ebrahim Zadeh* ; Mohammad Akhondi
Receive Date: 15 November 2015,  Revise Date: 22 January 2024,  Accept Date: 19 September 2018 
Abstract
Parameter estimation is an important task in the modeling, classification, and detection of radar
clutters. Radar clutters have stochastic characteristics. Therefore, Statistical distributions are
usually used to describe the features of clutters better. K distribution is one of the most common
models utilized to the simulation of clutters. This distribution, which consists of scale and shape
parameters, has two speckle and local power components. Because local power component is
modeled by gamma distribution, the parameter estimation of K distribution is a high
dimensional and nonlinear problem. In this paper, a novel method is proposed based on the
gravity searching algorithm for the parameter estimation. This new method has high accuracy
and validity in estimating parameters. For the evaluation of the proposed method, the estimated
probability density function and power spectrum in two different experiments were compared to
actual ones. Finally, the results of the new method are compared to the results of the maximum
likelihood method. Furthermore, K-S test is performed to evaluate generated clutters with
estimated parameters. Results prove the validity of the proposed method for the parameter
estimation.
Keywords
Gravity Searching Algorithm; K distribution; Parameter Estimation; Radar Clutter; K-S Test
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