Statistical Analysis of a High-Resolution Sea-Clutter Database

Carretero-Moya, Javier; Menoyo, Javier Gismero; Blanco-del-Campo, A.; López, A.

doi:10.1109/tgrs.2009.2033193

Cited by 138 publications

(50 citation statements)

References 41 publications

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“…Four moderately sized data regions have been selected from each data set with The ONERA data sets are first analyzed and then compared with the Ingara data set. For the MENAS data set, the shape and noise level for the P+N distribution were estimated using (12) and (13), respectively, while the alternate shape relationship in (10) was used for the other data sets, where the noise level is known.…”

Section: Resultsmentioning

confidence: 99%

Analysis of X-Band SAR Sea-Clutter Distributions at Different Grazing Angles

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Angelliaume

Rosenberg

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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International audienceModeling sea clutter is a difficult problem due to the interaction of the sea surface characteristics (wind speed and wind direction), the geometry of acquisition (grazing angle and azimuth angle), and radar parameters (frequency, polarization, and resolution). In synthetic aperture radar (SAR) imagery, the effect of coherent averaging and motion from the sea will also influence the statistics. An accurate description of the sea surface amplitude probability density function is important for robust target detection. The goal of this paper is to identify a common distribution which matches two different airborne SAR X-band data sets obtained from different locales and at different grazing angles. The first data set was collected by the French Aerospace Laboratory (ONERA) SETHI radar off the coast of France, at low grazing angles (3° and 10°). The second is the Ingara medium grazing angle (15°–45°) sea clutter data set collected by the Defence Science and Technology Organisation off the coast of Australia. In this paper, a number of recently developed distributions are considered, including the K+Rayleigh and Pareto+noise, both of which account for thermal noise. To measure the effectiveness of the model fit, the Bhattacharyya distance and the threshold error are computed with particular attention given to the tail region, where the threshold is determined in a detection scenario

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Section: Resultsmentioning

confidence: 99%

Analysis of X-Band SAR Sea-Clutter Distributions at Different Grazing Angles

Fiche

Angelliaume

Rosenberg

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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“…Traditionally, according to the central limit theorem, it has been assumed that the real and imaginary parts of the received data can be modeled by the Gaussian distribution. Although the Gaussian model fits accurately to the low-resolution sea-clutter, it does not perform correctly when the range resolution increases [5]. When dealing with high-resolution SAR systems, such as TerraSAR-X, the application of other distributions is needed.…”

Section: Statistical Distributionsmentioning

confidence: 99%

“…The goal of these studies was the modeling of either land or sea areas [4][5][6][7][8][9]. However, the goal of this paper is not the modeling of sea areas, but the classification of sea states using the information given by the statistical distributions and the impact the speckle has on sea clutter distribution.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of SAR Sea Clutter for Classification Purposes

et al. 2014

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Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

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“…Thus, the target detection problem can be formulated as the following binary hypothesis test: see (9) at the bottom of the page, where the subscript denotes the th mixer output.…”

Section: A Signal Model Of the Range-spread Targetmentioning

confidence: 99%

“…There are two reasons for this. First, the high resolution range cell contains less noise [9]. Most range-spread targets include a few strong physical scatterers [10], implying that range cells with strong physical scatterers are sparse in the HRRPs [11].…”

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confidence: 99%

CFAR Detection of Range-Spread Targets Based on the Time-Frequency Decomposition Feature of Two Adjacent Returned Signals

Zuo

Zhang

et al. 2013

IEEE Trans. Signal Process.

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Radar returned signals are processed by stretch processing, resulting in mixer outputs. Based on the time-frequency decomposition of the cross S-method (CSM) of two adjacent mixer outputs, a range-spread target detector is proposed in this paper. As a preparatory work, we propose a signal synthesis method (SSM) based on the singular value decomposition. The SSM synthesizes two signals in their normalized forms from their cross Wigner distribution (CWD) and concentrates their energy on two singular values. This detector consists of three steps. First, we derive the CSM from the S-method (SM). The CSM is close to the sum of the CWDs of the components in one mixer output and their counterparts in the other. Second, we can decompose the CSM by the SSM, thereby obtaining singular values. Third, the time-frequency decomposition feature, i.e., the ratio of the sum of several biggest singular values to the median or mean of the rest, is defined to demonstrate the concentration of the singular values and used to detect the range-spread target. The proposed detector is evaluated by the raw radar data without range migration correction. Results show that it outperforms the conventional detectors. In addition, we prove that the proposed detector has the constant false-alarm rate (CFAR) property.Index Terms-Cross S-method (CSM), constant false-alarm rate (CFAR), range-spread target detector, signal decomposition, singular values.

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Statistical Analysis of a High-Resolution Sea-Clutter Database

Cited by 138 publications

References 41 publications

Analysis of X-Band SAR Sea-Clutter Distributions at Different Grazing Angles

Analysis of X-Band SAR Sea-Clutter Distributions at Different Grazing Angles

Statistical Analysis of SAR Sea Clutter for Classification Purposes

CFAR Detection of Range-Spread Targets Based on the Time-Frequency Decomposition Feature of Two Adjacent Returned Signals

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