Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Wang, Jianbo; Xu, Xiaojian

doi:10.1109/tgrs.2014.2388211

Cited by 31 publications

(14 citation statements)

References 35 publications

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“…In these cases, having a comprehensive understanding for the characteristic of the clutter Doppler spectrum is very helpful in designing specific signal processing algorithms for clutter suppression and for target detection [53][54][55][56]. Moreover, the proper model of the clutter Doppler spectrum is essential for the accurate simulation of the coherent clutter returns [32,33,37]. In this section, the Doppler spectrum properties of the recorded sea clutter are discussed.…”

Section: Spectral Analysismentioning

confidence: 99%

“…The normalized PSDs are achieved by normalizing the spectrum intensity to a peak value of unity. The form of this Gaussian-shaped PSD can be expressed as [33,37,38,58]…”

Section: Spectral Analysismentioning

confidence: 99%

“…The correlation characteristics of sea clutter, which are particularly important for modeling and simulating clutter sequences and predicting detection performance involving temporal or spatial signal processing, include the temporal and spatial correlations [32][33][34][35][36]. In addition, many distribution models are special cases of the compound Gaussian model; thus, many simulations of sea clutter and target detection are performed on the assumption of the compound Gaussian model.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter

Hong

Wang

Tai

et al. 2017

International Journal of Antennas and Propagation

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A statistical analysis that properly characterizes sea clutter processes is indispensable both for optimum detection algorithm design and for performance prediction problems in maritime surveillance applications. In this paper, we present the statistical analysis of experimental sea clutter data collected by a high-resolution coherent monopulse radar. First, we present the amplitude statistical analyses for these clutter data. The results show that the K, Pareto, and CIG distributions can each provide good fits to the clutter data for three channels of monopulse radar. The analyses on the variations of the K distribution parameters with range suggest that the scale parameter is closely associated with the clutter powers and that the shape parameter is influenced by the sea state. Then, we focus on the correlation properties. The averaged results suggest that the temporal and spatial correlation properties are similar for the clutter of all three channels. Moreover, the clutter between the sum and difference channels is almost completely correlated in elevation and is lowly correlated in azimuth. Finally, we perform a spectral analysis, highlighting the temporal and spatial variabilities of Doppler spectra. It is found that the individual Doppler spectra in all three channels can be represented by Gaussian-shaped power spectral densities, and their centroid and width can be modeled as two separate stage linear functions of spectrum intensity.

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Section: Spectral Analysismentioning

confidence: 99%

“…The normalized PSDs are achieved by normalizing the spectrum intensity to a peak value of unity. The form of this Gaussian-shaped PSD can be expressed as [33,37,38,58]…”

Section: Spectral Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter

Hong

Wang

Tai

et al. 2017

International Journal of Antennas and Propagation

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“…However, sea clutter data in the actual collection usually do not conform to the specific precise model. Therefore, the method of clutter simulation based on a specific parameter model is particularly important in radar system analysis and performance verification [3,4]. Because of the complexity and rapidity of the sea condition, the research of sea surface remote sensing also presents the development trend from monostatic radar to multistatic radar [5,6], from conventional band radar to microwave radar [7], and from traditional radar to multi-element radar [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with Non-Integral Shape Parameters

Chen

2020

Sensors

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Sea clutter simulation is a well-known research endeavour in radar detector analysis and design, and many approaches to it have been proposed in recent years, among which zero memory non-linear (ZMNL) and spherically invariant random process (SIRP) are the most two widely used methods for compound Gaussian distribution. However, the shape parameter of the compound Gaussian clutter model cannot be a non-integer nor non-semi-integer in the ZMNL method, and the computational complexity of the SIRP method is very high because of the complex non-linear operation. Although some improved methods have been proposed to solve the problem, the fitting degree of these methods is not high because of the introduction of Beta distribution. To overcome these disadvantages, a novel Gamma distributed random variable (RV) generation method for clutter simulation is proposed in this paper. In our method, Gamma RV with non-integral or non-semi-integral shape parameters is generated directly by multiplying an integral-shape-parameter Gamma RV with a Beta RV whose parameters are larger than 0.5, thus avoiding the deviation of simulation of Beta RV. A large number of simulation experimental results show that the proposed method not only can be used in the clutter simulation with a non-integer or non-semi-integer shape parameter value, but also has higher fitting degree than the existing methods.Sensors 2020, 20, 955 2 of 11 of its large computation. A method based on zero memory non-linear (ZMNL) is proposed in [12]. The ZMNL is suitable for non-coherent clutter model, and the principle of it is simple. However, the main drawback of ZMNL is that the shape parameter of the clutter model must be an integer or semi-integer. If the shape parameter does not satisfy the requirement, the ZMNL method will lead to clutter simulation error. What's worse, the ZMNL cannot control the power spectrum and amplitude independently. With the principle that each quadrature component of K-distributed clutter can be modelled, exactly or approximately, by a weighted sum of products of two independent Gaussian variables in [13], a new method for modelling and simulation of correlated K-distributed clutter is proposed in [14]; the new method can simulate the clutter with arbitrary and specified power spectrum, and is a much more simple calculation compared with the traditional ZMNL and SIRP, which makes it easier to realize both in software and hardware. Nevertheless, an approximate approach is made for the simulation when the shape parameter is non-integer or non-semi-integer. Besides, a method of simulation of coherent compound Gaussian clutter based on memorials non-linear transformation (MNLT) is proposed in [15]. Through the generation of arbitrary correlated Gamma random variable (RV) by MNLT, this method can simulate clutter specifically. However, similar to SIRP method, the computational complexity of MNLT is high due to nonlinear computation.To solve this problem, the additive property of Gamma RV is used to improve the ZMNL and SIRP methods in [16,17...

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“…A relatively complete method of reconstructing spatial-temporal correlated K-distributed sea clutter was proposed in [1], where the spatial and temporal correlations were individually controlled, respectively. In [29], two methods of simulating temporally correlated sea clutter in short term and long term were discussed separately. The spatial and temporal correlations of clutter were considered to be independent there; however, the texture component of clutter is spatial-temporal correlated in the long-term and there is complex coupling between the spatial and temporal correlations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial–Temporal Correlation

Ding

Quan

et al. 2020

Sensors

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The reconstruction of sea clutter plays an important role in target detection and recognition in a maritime environment. Reproducing the temporal and spatial correlations of real data simultaneously is always a problem in the reconstruction of sea clutter due to the complex coupling between them. In this paper, the spatial–temporal correlated proportional method (STCPM), based on a compound model, is proposed to reconstruct K-distributed sea clutter with correlation characteristics obtained from the real data. The texture component with spatial–temporal correlation is generated by the proportional method and the speckle component with temporal correlation is generated by matrix transformation. Compared with previous methods, the biggest innovation of the STCPM is that it can more accurately generate K-distributed sea clutter with both temporal and spatial correlations. The comparison of the reconstructed and real data demonstrates that the method can reproduce the characteristics of real sea clutter well.

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Simulation of Correlated Low-Grazing-Angle Sea Clutter Based on Phase Retrieval

Cited by 31 publications

References 35 publications

Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter

Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter

A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with Non-Integral Shape Parameters

A Novel Reconstruction Method of K-Distributed Sea Clutter with Spatial–Temporal Correlation

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