Analysis of K-distributed sea clutter and thermal noise in high range and Doppler resolution radar data

Sutour, Camille; Petitjean, Julien; Watts, Simon; Quellec, Jean-Michel; Kemkemian, Stephane

doi:10.1109/radar.2013.6586052

Cited by 7 publications

(12 citation statements)

References 7 publications

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“…The mean and variance of sea clutter are closely related to the sea state. The sea clutter distribution model of major theoretic and practical interest is the so-called K -distributed clutter model [24,25,26], and the PDF (probability distribution function) is Equation (2). fcfalse(pcfalse)=2bΓfalse(v+1false)false(bpc2false)v+1Kvfalse(bpcfalse), where p c denotes the power of the sea clutter in this cell, Γ( v ) is the gamma function, ν is referred to as the shape parameter, b is a scale parameter, and K v ( u ) denotes the modified Bessel function of second kind and order.…”

Section: Models and Notationsmentioning

confidence: 99%

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Yan

Zhao

et al. 2019

Sensors

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Excellent performance, real-time and low memory requirement are three vital requirements for target detection in high resolution marine radar system. Unfortunately, many current state-of-the-art methods merely achieve excellent performance when coping with highly complex scenes. In fact, a common problem is that real-time processing, low memory requirement and remarkable detection ability are difficult to coordinate. To address this issue, we propose a novel detection framework which bases its principle on sampling and spatiotemporal detection. The framework consists of two stages, coarse detection and fine detection. Sampling-based coarse detection is designed to guarantee the real-time processing and low memory requirements by locating the area where targets may exist in advance. Different from former detection methods, multi-scan video data are utilized. In the stage of fine detection, the candidate areas are grouped into three categories: single target, dense targets and sea clutter. Different approaches for processing the different categories are implemented to achieve excellent performance. The superiority of the proposed framework beyond state-of-the-art baselines is well substantiated in this work. Low memory requirement of the proposed framework was verified by theoretical analysis. Real-time processing capability was verified by the video data of two real scenarios. Synthetic data were tested to show the improvement in tracking performance by using the proposed detection framework.

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Section: Models and Notationsmentioning

confidence: 99%

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Yan

Zhao

et al. 2019

Sensors

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“…In (24), the logarithm of the Whittaker function is too difficult to handle, the equivalent expression is given here [30]:…”

Section: B Kpn Model Estimator Based On Molcmentioning

confidence: 99%

“…Watts [23] made further predication on radar detection prediction in K-distributed sea clutter and thermal noise and proposed a K-distributed plus thermal noise estimator which uses higher-order moments estimation (HOME) in. Sutour et al [24] analyzed the K-distributed sea clutter and thermal noise in high range and Doppler resolution radar data and put forward a parameter estimator by calculating the complementary cumulative density function of real data in. Mezache et al [25] introduce two pragmatic methods called curve-fitting estimation and Nelder-Mead algorithm to parameter estimation for KpN model.…”

Section: Introductionmentioning

confidence: 99%

Mellin Transform-Based Approach for Parameter Estimation of K Distribution Plus Noise

2019

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Mellin transform is a fast and robust method for parameter estimation. In this paper, the parameter estimator of K plus noise (KpN) model based on the Mellin transform is proposed. First, we start deriving the expression of the second characteristic functions and log-based moments in detail by Mellin transform. And then, we provide the expression of the first fourth order log-culumants which are directly related to the parameters of KpN model. Finally, the numerical computation is applied to realize parameter estimation of KpN model. The Monte Carlo simulations show that the proposed estimator is more efficient than existing methods for clutter to noise ratios under both situations of large and small sample sizes. INDEX TERMS K plus noise model, parameters estimation, Mellin transform, synthetic aperture radar (SAR).

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“…The noise consists of two parts, sea clutter Z S and thermal noise V. The mean and variance of sea clutter is related to the sea state closely. The sea clutter distribution model of major theoretic and practical interest is the so-called K-distributed clutter model, 6 and the PDF of Z s is given by the following equation…”

Section: Target Modelmentioning

confidence: 99%

“…The compound K-distribution model given in Ward 4 is applicable for high-resolution sea clutter. 5 And in Sutour et al 6 and McDonald et al, 7 the K-distribution model in a maritime environment is described in detail. Based on the K-distributed clutter, a significant texture fluctuation caused by the continuous antenna scanning motion is considered in Roy and Kumar.…”

Section: Introductionmentioning

confidence: 99%

An efficient extended target detection method based on region growing and contour tracking algorithm

Yan

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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Extended target detection in the presence of K-distributed clutter has gained a special interest in recent years. Highresolution radars allow a target to be found in several resolution cells. Therefore, the detection rate and the false alarm rate of an extended target should be analyzed by these cells jointly rather than in one single resolution cell. A detector in which all the cells whose magnitude affected by one target are considered jointly is present. The performance and optimal parameters of the detector are analyzed in detail. Meanwhile, the large amount of calculation caused by enormous raw data is also considered. Then, an efficient method based on region growing algorithm and contour tracking algorithm is proposed. Only part of the resolution cells is scanned once with the proposed method, while all the cells are scanned at least one times in the existing methods. Therefore, considerable calculations are saved. Furthermore, the proposed method and several existing methods are performed with real data and simulated data, and the results show that the proposed model is practical and efficient to detect the extended targets in K-distributed clutters.

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Analysis of K-distributed sea clutter and thermal noise in high range and Doppler resolution radar data

Cited by 7 publications

References 7 publications

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

An Efficient Extended Targets Detection Framework Based on Sampling and Spatio-Temporal Detection

Mellin Transform-Based Approach for Parameter Estimation of K Distribution Plus Noise

An efficient extended target detection method based on region growing and contour tracking algorithm

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