Low-Velocity Small Target Detection With Doppler-Guided Retrospective Filter in High-Resolution Radar at Fast Scan Mode

Shi, Sai-Nan; Liang, Xiang; Shui, Peng‐Lang; Zhang, Jiankang; Zhang, Shuai

doi:10.1109/tgrs.2019.2923790

Cited by 24 publications

(15 citation statements)

References 40 publications

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“…This paper focuses on sea clutter modelling and moving small target detection in maritime surface surveillance radars. For small target detection [3,4,14], a valid and feasible technique to enhance signal-to-clutter ratio (SCR) is to match the range resolution of radar to the size of interested small targets [3]. As a result, high range resolution leads to heterogeneity and non-Gaussianity of sea clutter, which degrades the detection performance of existing adaptive coherent detection methods due to model mismatch, especially in moderate and high sea states [15].…”

Section: Introductionmentioning

confidence: 99%

“…In the CGM, complex sea clutter time series from a spatial resolution cell is modeled as a product of a fast-varying complex Gaussian speckle random series and a slow-varying positive texture random series, and the two series are mutually independent [16]. In a coherent processing interval (CPI) of tens of milliseconds, the texture process is reduced to a random constant, and the CGM degenerates to the spherical invariant random vector (SIRV) model [14]. Different types of texture distributions are successively introduced, such as Gamma distributions, inverse Gamma distributions, log-normal distributions, and inverse Gaussian distributions [11].…”

Section: Introductionmentioning

confidence: 99%

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Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

Liang

Shui

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In high-resolution maritime surveillance radars, sea clutter exhibits highly spatial heterogeneity due to modulation of long waves with wavelengths longer than the width of one range cell. Compound-Gaussian model (CGM) fails to characterize the heterogeneous high-resolution sea clutter in both amplitude distribution and Doppler spectrum. In this paper, a bi-phase compound-Gaussian mixture model (BP-CGMM) is proposed to characterize the heterogeneous sea clutter. In the BP-CGMM, spatial resolution cells are grouped into two disjoint sets, and the sea clutter in each set is represented by one CGM with inverse Gamma-distributed texture. The spectral heterogeneity indicates that sea clutter vectors at spatially adjacent resolution cells in one set share the same speckle covariance matrix, while that at two adjacent spatial cells separated in the two sets often have different speckle covariance matrices. The BP-CGMM is validated by a mass of measured high-resolution sea clutter data. Moreover, under the BP-CGMM, a detection method based on batch test is given to detect sea-surface small targets, which is composed of scene segmentation, by the aid of Bayesian threshold and morphological filtering, and adaptive generalized likelihood ratio test linear-threshold detector (GLRT-LTD) separately in each set. The detection method is verified by measured data with small targets under test. The experimental results show that it attains better detection performance than the adaptive GLRT-LTD under the CGM of sea clutter. Index Terms-Modulation of long waves; Heterogeneous high-resolution sea clutter; Compound-Gaussian mixture model; Spectral heterogeneity; Scene segmentation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

Liang

Shui

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The statistical model of the dynamic sea clutter is the basic model of the returns from radar, which is an important tool in the understanding of maritime radars. Recently, the temporal variations of sea clutter increasingly play a significant role in dynamic sea surface electromagnetic scattering and target detection [2] [3]. There are two categories of sea clutter models: the amplitude statistical approach-based models and models based on the electromagnetic (EM) [4].…”

Section: Introductionmentioning

confidence: 99%

Fast Realization of 3-D Space-Time Correlation Sea Clutter of Large-Scale Sea Scene Based on FPGA: From EM Model to Statistical Model

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Besides the lack of knowledge about the statistical model there is the problem of insufficient discrimination ability of the variables used during detection. It is a general criterion that the parameters of the eco-signals should be processed to increase their possibilities [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Selection of Sampling Interval and Size of Random Sample for Radar Detection in the Moments Space

Guillén

Rodríguez

Chávez

2021

Period. Polytech. Elec. Eng. Comp. Sci.

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Recently a novel method for radar detection was conceived to process the scattered signal parameters and detect through its statistical moments. Among the advantages of detection in the moments space stands the opportunity of considering the moments like Gaussian random variables, decreasing the uncertainty about the distribution of the variables used by traditional methods. Therefore, it is very important to study the conditions for assuming the above within certain level of confidence. This work uses real radar signals in order to study the influence of two essential variables for detection in the moments space: the sampling interval and the size of the random sample. Average correlation coefficient, hypothesis testing and numerical goodness-of-fit coefficients are used to estimate the values of the previous variables that allow to take the joint distribution of moments as close to the multivariate Gaussian. The guidelines presented should be taken into account for the proper configuration of detectors in the moments space.

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Low-Velocity Small Target Detection With Doppler-Guided Retrospective Filter in High-Resolution Radar at Fast Scan Mode

Cited by 24 publications

References 40 publications

Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

Bi-Phase Compound-Gaussian Mixture Model of Sea Clutter and Scene-Segmentation-Based Target Detection

Fast Realization of 3-D Space-Time Correlation Sea Clutter of Large-Scale Sea Scene Based on FPGA: From EM Model to Statistical Model

Selection of Sampling Interval and Size of Random Sample for Radar Detection in the Moments Space

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