Characterization of fluctuation statistics of radar clutter for Indian terrain

Menon, K. R.; Balakrishnan, N.; Janahraman, M.; Ramchand, K.

doi:10.1109/tgrs.1995.8746012

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…We characterize the convergence property by the iteration error, which is defined as e i = g − Hf i 2 (25) where e i means the iteration error after i iterations and f i is the estimation of imaging scene amplitude values after i iterations. Fig.4(a) shows a plot of the normalized iterative error curves in Rayleigh distribution when the SCR is 20 dB.…”

Section: A Simulation Results Of Point Targetsmentioning

confidence: 99%

“…It has been verified in [30] and [31] that Rayleigh assumption is well suited for describing the statistic property of sea clutter. Moreover, the Rayleigh-based ML approach in [25] has demonstrated the feasibility of superresolution imaging for sea-surface target.…”

Section: Bayesian Superresolution Methodsmentioning

confidence: 99%

“…Researches show that the distribution of sea clutter approximately obeys K distribution [22], Rayleigh distribution [23] and Log-normal distribution [24]. In reference [25], the authors presented a maximum likelihood (ML) approach, whose likelihood function is Rayleigh distribution. The simulation results show the effective improvement of azimuth resolution under the background of sea clutter, but the property of clutter suppression and imaging quality is insufficient due to the lack of prior information.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

et al. 2020

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Maximum a posteriori (MAP) approach, based on Bayesian criterion, is proposed to overcome the low azimuth resolution in real-aperture imaging. The essence of this approach is to use the statistical characteristics of the imaging background and target to invert the real target scene. This paper presents a deconvolution method based on Maximum a posteriori (MAP) criterion, which combines the Rayleigh distribution and Lognormal distribution, to realize high angular resolution for sea-surface target. Firstly, Rayleigh distribution is considered to express the statistical properties of sea clutter. Moreover, the Lognormal distribution is employed to represent the statistical properties of target as prior information. The reason is that Lognormal distribution can be approximatively regarded as a combined constraint term. Finally, the optimization theory is utilized to obtain the iterative estimated solution. The processed results of simulation and measured data are given to verify the performance of proposed algorithm.

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Section: A Simulation Results Of Point Targetsmentioning

confidence: 99%

Section: Bayesian Superresolution Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

et al. 2020

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“…The LN statistical distribution has had an extensive application in radars issues. It is widely used for both sea [12], [13], [16], [22] and ground clutter [23]- [25]. Even if the model prevails sometimes for a complete set of low grazing angle measurements [16], [26], its best fit generally appears for sub-sets with HH polarization [27], when measuring the spatial data spatial distribution [25], and for cells containing big targets reflections [16], [28].…”

Section: The Log-normal Statistical Distribution and The Mommentioning

confidence: 99%

Cell Averaging CFAR Detector with Scale Factor Correction through the Method of Moments for the Log-Normal Distribution

Fernández

Vidal

2017

Cien.Ing.Neogranadina

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Se presenta el nuevo detector LN-MoM-CA-CFAR que tiene una desviación reducida en la tasa de probabilidad de falsa alarma operacional con respecto al valor concebido de diseño. La solución corrige un problema fundamental de los procesadores CFAR que ha sido ignorado en múltiples desarrollos. En efecto, la mayoría de los esquemas previamente propuestos tratan con los cambios bruscos del nivel del clutter mientras que la presente solución corrige los cambios lentos estadísticos de la señal de fondo. Se ha demostrado que estos tienen una influencia marcada en la selección del factor de ajuste multiplicativo CFAR, y consecuentemente en el mantenimiento de la probabilidad de falsa alarma. Los autores aprovecharon la alta precisión que se alcanza en la estimación del parámetro de forma Log-Normal con el MoM, y la amplia aplicación de esta distribución en la modelación del clutter, para crear una arquitectura que ofrece resultados precisos y con bajo costo computacional. Luego de un procesamiento intensivo de 100 millones de muestras Log-Normal, se creó un esquema que, mejorando el desempeño del clásico CA-CFAR a través de la corrección continua de su factor de ajuste, opera con una excelente estabilidad alcanzando una desviación de solamente 0,2884 % para la probabilidad de falsa alarma de 0,01.

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“…In addition, the shift of the distribution with increasing grazing angle towards lower attenuation values was worked out. The influence of an illumination with a low grazing angle range for Indian rural terrain on the statistical clutter properties is presented in [23]. A comprehensive database considering different clutter density functions can be found in [24] also for snow covered terrain.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Bistatic Radar Ground Clutter for Different German Rural Environments

Köhler

O’Hagan

Weiß

et al. 2020

Sensors

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This article presents the statistical analysis of bistatic radar rural ground clutter for different terrain types under low grazing angles. Compared to most state-of-the-art analysis, we present country-specific clutter analysis for subgroups of rural environments rather than for the rural environment as a whole. Therefore, the rural environment analysis is divided into four dominant subgroup terrain types, namely fields with low vegetation, fields with high vegetation, plantations of small trees and forest environments representing a typical rural German environment. We will present the results for both the summer and the winter vegetation. Therefore, bistatic measurement campaigns have been carried out during the summer 2019 and the winter of 2019/20 in the aforementioned four different rural terrain types. The measurements were performed in the radar relevant X-band at a center frequency of 8.85 GHz and over a bandwidth of 100 MHz according to available transmit permission. The distinction of the rural terrain into different subgroups enables a more precise and accurate clutter analysis and modeling of the statistical properties as will be shown in the presented results. The statistical properties are derived from the calculated clutter amplitudes probability density functions and corresponding cumulative distribution functions for each of the four terrain types and the corresponding season. The data basis for the clutter analysis are the processed range-Doppler maps from the bistatic radar measurements. According to the authors’ current knowledge, a similar investigation based on real bistatic radar measurement data with the division into terrain subgroups has not yet been carried out and published for a German rural environment.

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Characterization of fluctuation statistics of radar clutter for Indian terrain

Cited by 8 publications

References 2 publications

A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

Cell Averaging CFAR Detector with Scale Factor Correction through the Method of Moments for the Log-Normal Distribution

Statistical Analysis of Bistatic Radar Ground Clutter for Different German Rural Environments

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