Local Mean Square Research Articles

A novel deformation measurement method for rotating blade based on PSO-ILS image correlation matching and mismatch correction

Abstract Aiming at the problem of small measurement range and difficult measurement of traditional contact sensor method in the rotating state of aero-engine blades, this paper proposes a novel deformation measurement method for rotating blade based on image correlation matching and mismatch correction. Firstly, a Particle Swarm Optimization-Iterative Local Search image intelligent matching algorithm is proposed, which effectively balances global search and local optimization, and the image matching displacement accuracy reaches 10−3 pixel. Secondly, a mismatch point detection method based on multi-scale local Root Mean Square is proposed, and the high-precision detection of mismatch points is realized by considering the influence of local structure. Finally, through the hierarchical refinement of the sub-pixel level mismatch point correction method, the mismatch point is corrected in the two iterative stages of global search and local optimization. The corrected mismatch point response value K is reduced by 99% compared with that before correction, which further improves the accuracy of deformation field calculation. In the experiment, the deformation of the rotating blade of 4500 RPM is measured, which proves that the image intelligent matching algorithm and the deformation field calculation method proposed in this paper can provide new methods and technical support for the accurate measurement of the blade surface deformation.

Toward the measurement of neutrino masses: Performance of cosmic magnification with submillimeter galaxies

Context. The phenomenon of magnification bias can induce a non-negligible angular correlation between two samples of galaxies with nonoverlapping redshift distributions. This signal is particularly clear when background submillimeter galaxies are used, and has been shown to constitute an independent cosmological probe. Aims. This work extends prior studies on the submillimeter galaxy magnification bias to the massive neutrino scenario, with the aim being to assess its sensitivity as a cosmological observable to the sum of neutrino masses. Methods. The measurements of the angular cross-correlation function between moderate redshift GAMA galaxies and high-redshift submillimeter H-ATLAS galaxies are fit to the weak lensing prediction down to the arcmin scale. The signal is interpreted under the halo model, which is modified to accommodate massive neutrinos. We discuss the impact of the choice of cosmological parametrization on the sensitivity to neutrino masses. Results. The currently available data on the magnification bias affecting submillimeter galaxies are sensitive to neutrino masses when a cosmological parametrization in terms of the primordial amplitude of the power spectrum (As) is chosen over the local root mean square of smoothed linear density perturbations (σ8). A clear upper limit on the sum of neutrino masses can be derived if the value of As is either fixed or assigned a narrow Gaussian prior, a behavior that is robust against changes to the chosen value.

Statistical Analysis of the Surface Roughness on Aircraft Icing

A statistical analysis of the surface roughness is performed on experimentally obtained ice shapes on an asymmetrical airfoil at Rec≈2⋅106. The ice shapes were generated in the Icing Wind Tunnel of the Technical University of Braunschweig under Appendix C and O conditions of the EASA airplane certification standards as part of the ICE GENESIS project. The photogrammetry method is used for the digitization of the experimental ice shapes, while statistical parameters such as the mean ice shape and the local root mean square (RMS) of the ice geometry are extracted using a traditional surface projection method, as well as a self-organizing maps approach. Results show the evolution of the statistical parameters over time and the influence of the freestream static temperature on these parameters. A comparison between the experimental values of the local RMS of the ice geometry and a correlation for roughness prediction is presented, showing a good match with the original formulation of the correlation for cases under Appendix C conditions while having a good match with Appendix O conditions when a temperature correction factor is applied to the formulation. Additionally, results show an almost linear growth of roughness over the whole accretion time.

Simulation analysis of visual perception model based on pulse coupled neural network

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

Online monitoring of the voltage stability margin using local minimax concave penalty regression and adaptive database

A novel online monitoring method for the voltage stability margin (VSM) based on local minimax concave penalties (LocMCP) regression and adaptive database is proposed to ameliorate the weak model interpretability and insufficient generalization ability of the existing online monitoring methods. In this paper, the minimax concave penalty (MCP) is applied for VSM online monitoring for the first time. Compared with the multiple linear regression model (MLRM) and the local least absolute shrinkage and selection operator (LocLASSO), better prediction accuracy can be obtained through LocMCP. The MCP and local regression are combined to find the mapping relationship between VSM and reactive power reserves (RPRs) and to obtain the VSM online monitoring model composed of various local models. To further enhance the generalization ability of the model, an adaptive database is proposed. The data updating of the database is triggered when the local root mean square error (LocRMSE) exceeds the limit or is triggered in the case that the planned operation is performed. Furthermore, the local model can be updated according to the data updating results of the database. The 3-bus system, IEEE 30-bus system, and 1951-bus system are selected for verification, and the test results show that the proposed method is effective and has better generalization ability compared with other parameter regression methods.

Effect of expansion on the wall heat flux in a supersonic turbulent boundary layer

Direct numerical simulation of a spatially developing supersonic turbulent boundary layer at a Mach number of 2.25 and a friction Reynolds number of Reτ = 769 subjected to an expansion corner with a deflection angle of 12° is performed to investigate the effect of expansion on the characteristics of the wall heat flux (WHF). The effect of expansion on the statistical and structural properties of the fluctuating WHF is analyzed systematically in terms of probability density function, frequency spectra, and space-time correlations. Normalization using the local root mean square value yields good collapse of the probability density function curves. Unlike with wall pressure frequency spectra, it is found that expansion has little influence on the low-frequency components of the WHF spectrum. The correlation results show that the main effect of expansion is to increase the characteristic length scales and convection velocity of the WHF fluctuation in the post-expansion region. Furthermore, a direct comparison between the conditionally averaged flow fields and those presented in the authors' previous work [Tong et al., Phys. Fluids 34, 015127 (2022)] is performed to uncover the effect of expansion on the flow structures associated with extreme positive and negative WHF fluctuation events. We highlight that the extreme positive event emerges below a small hot spot under the action of a strong Q4 event, whereas the extreme negative event is relatively insensitive to expansion and still occurs between a pair of strong oblique vortices. In addition, we decompose the mean WHF into seven physics-informed contributions and quantify the effect of expansion on the dominating components with the aid of the bidimensional empirical mode decomposition method. The scale-decomposed results demonstrate quantitatively that expansion decreases the contribution of the large-scale structures in the outer region but the small-scale structures in the near-wall region contribute heavily to the mean WHF generation in the downstream region.

Cell-Free Massive MIMO-OFDM for High-Speed Train Communications

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems show great potentials in low-mobility scenarios, due to cell boundary disappearance and strong macro diversity. However, the great Doppler frequency offset (DFO) leads to serious inter-carrier interference in orthogonal frequency division multiplexing (OFDM) technology, which makes it difficult to provide high-quality transmissions for both high-speed train (HST) operation control systems and passengers. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both fully centralized and local minimum mean square error (MMSE) combining in HST communications. Considering the local maximum ratio (MR) combining, the large-scale fading decoding (LSFD) cooperation and the practical effect of DFO on system performance, exact closed-form expressions for uplink spectral efficiency (SE) expressions are derived. We observe that cooperative MMSE combining achieves better SE performance than uncooperative MR combining. In addition, HST communications with small cell and cellular massive MIMO-OFDM systems are compared in terms of SE. Numerical results reveal that the CF massive MIMO-OFDM system achieves a larger and more uniform SE than the other systems. Finally, the train antenna centric (TA-centric) CF massive MIMO-OFDM system is designed for practical implementation in HST communications, and three power control schemes are adopted to optimize the propagation of TAs for reducing the impact of the DFO.

Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor

The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi‐coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma

Limited-Fronthaul Cell-Free Massive MIMO With Local MMSE Receiver Under Rician Fading and Phase Shifts

A cell-free Massive multiple-input multiple-output (MIMO) system is considered, where the access points (APs) are linked to a central processing unit (CPU) via the limited-capacity fronthaul links. It is assumed that only the quantized version of the weighted signals are available at the CPU. The achievable rate of a limited fronthaul cell-free massive MIMO with local minimum mean square error (MMSE) detection is studied. We study the assumption of uncorrelated quantization distortion, which is commonly used in literature. We show that this assumption will not affect the validity of the insights obtained in our work. To investigate this, we compare the uplink per-user rate with different system parameters for two different scenarios; 1) the exact uplink per-user rate and 2) the uplink per-user rate while ignoring the correlation between the inputs of the quantizers. Finally, we present the conditions which imply that the quantization distortions across APs can be assumed to be uncorrelated.

Distributed Covariance Intersection Fusion Estimation With Delayed Measurements and Unknown Inputs

This article is concerned with the distributed covariance intersection (CI) fusion estimation for cyber-physical systems (CPSs) with delayed measurements and unknown inputs. The measurement transmission is subject to random delays described by a set of independent Bernoulli processes. Based on the provided finite-length buffers, the delayed measurements are retrieved within the corresponding buffer length. By modeling the unknown inputs with a noninformative prior distribution, a local minimum mean square error (MMSE) estimator is derived in the Bayesian framework. Then this result is extended to the multiple sensor scenario, where the sequential CI fusion approach is applied to design a recursively distributed fusion estimator. It is proved that the distributed sequential CI fusion estimator is consistent and performs better than each local estimator in state estimation. An illustrative example is provided to demonstrate the effectiveness of the proposed technique.

Peak-locking full characterization: PIV error assessment and velocity ensemble measurement correction

Concluding a few previous approaches, the last work by the authors presented a complete analytic model for peak-locking error. That work was devoted to the mathematical foundation of the model. This paper separately focuses on the procedure for calibrating a given particle-image velocimetry measurement setup using the model. In addition to error assessment, it allows for the measurement correction of statistical ensembles such as: (a) the local velocity average and (b) the local velocity root mean square (RMS). This calibration is done by means of an inexpensive multiple Δt strategy, consisting of measuring the same flow field with different Δt subsets. This paper offers a comparison with previous empirical models produced by the authors, showing the advantages of the robust theoretical foundation established for this model. A real case corresponding to a hot high-speed coaxial jet, representative of the turbofan engine propulsion systems used for civil transport aircraft, is used for this comparison. The discrepancy plots between the different Δt subsets indicate that the peak-locking error description and correction are improved by 30% for the local velocity average. For the local velocity RMS, the results are even more striking, as the peak locking error can be almost completely corrected in situations where previous models only provided the order of magnitude of the error. Together with a detailed explanation of the calibration procedure, the paper offers guidelines to avoid getting into an ill-conditioned mathematical problem.

Maximum mixture total correntropy adaptive filtering against impulsive noises

To solve the problem that the performance of maximum total correntropy (MTC) algorithm is limited by kernel width, a more flexible algorithm called maximum mixture total correntropy (MMTC) is proposed in this brief, which is derived by combining the mixture correntropy (MC) and total least square (TLS) method. Then, the local mean and mean square performance of the MMTC algorithm are analyzed in detail under some simplifying assumptions, the stable step size range and theoretical steady-state MSD expression are also given. Numerical simulations exhibit that the MMTC performs better than MTC for the parameter estimation of the error-in-variable (EIV) model in the background of impulse noise, the validity of the theoretical analysis is also verified.

Binocular stereo vision calibration based on accurate ellipse detection algorithm of direct calculation and grating conversion check

With the rapid development of science and technology, binocular vision detection technology has become one of the most important technologies because of its high precision and stability. In order to achieve high-precision three-dimensional measurement of high-speed rail train, a binocular stereo vision system is built in this paper. The visual image is gray-scale processed, and then the integral image is used to realize the edge preservation processing of local mean square deviation, and then the denoised image is enhanced. At the same time, FAST algorithm based on accelerated segmentation detection is proposed to extract feature points. In order to solve the problem of camera calibration, this paper proposes a high-precision binocular stereo vision calibration algorithm based on direct calculation and grating conversion calibration. Firstly, the reliable information points are determined from the two-dimensional image, and then the edge image is obtained by effectively detecting the ellipse contour according to the set of information points. Finally, the ellipse parameters are extracted by using the ellipse contour information, and finally the camera parameters are solved by using the ring point. Compared with the traditional calibration method, the experimental results show that the average reprojection error of the two cameras is about 0.1 pixel, and the data accuracy is high, which proves the feasibility and theoretical correctness of the experimental method.

A multiverse optimization based colour image segmentation using variational mode decomposition

Multilevel thresholding using the histogram is the most popular and accepted technique of image segmentation. The computational time of multilevel thresholding rises exponentially as the number of the thresholds increases. Histogram suffers from irregularities and sharp details which leads to stagnation. In this article, a newly developed multiverse optimization is combined with variational mode decomposition to overcome this problem. The histogram of an input image divided into several band-limit modes using VMD then reconstruct histogram using meaningful modes. The reconstructed histogram is free from the high-frequency fluctuation which causes local optima. The proposed method utilized two entropy function to develop image segmentation by determining the optimal threshold. The result of the proposed algorithm is analyzed with other evolutionary algorithms such as artificial bee colony, sine cosine algorithm, and salp swarm algorithm. Comparison is made based on comparative parameters such as peak signal to noise ratio, structural similarity index, feature similarity index, uniformity, normalized absolute error, quality index based on local variance, computational time, and mean square error. The test results validate that the proposed algorithm presents more reliable results than other existing techniques.

Poisson-Gaussian Noise Reduction for X-Ray Images Based on Local Linear Minimum Mean Square Error Shrinkage in Nonsubsampled Contourlet Transform Domain

Noise reduction is important for X-ray images because it can reduce radiation exposure to patients. X-ray image noise has a Poisson-Gaussian distribution, and recently, noise analysis and removal in multiscale transformations have been widely implemented. The nonsubsampled contourlet transform (NSCT) is a multiscale transformation suitable for medical images that separates the scale and direction. This study proposes a Poisson-Gaussian noise-removal method using NSCT shrinkage that is based on the characteristics of Poisson-Gaussian noise in NSCT domain. It has the structure of a block-matching 3D filtering algorithm in the form of basic estimation and noise removal process; however, the main processes are modified to consider Poisson-Gaussian noise characteristics. In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. In the denoising step, the noise term of the Wiener filter is determined using the result of the NSCT shrinkage, and finally, the denoised image is obtained. The proposed method is applied to simulated and real X-ray images and is compared with other state-of-the-art Poisson-Gaussian noise removal methods; it exhibits excellent results in both quantitative and qualitative aspects.

An Improved Method for Localization of Wireless Capsule Endoscope Using Direct Position Determination

A novel methodology for localization of wireless-capsule-endoscope (WCE) by using the direct-position-determination (DPD) method is proposed. A WCE enables the diagnose of gastrointestinal tract disorders in a non-invasive visual manner. Conventional methods proposed for WCE localization are not optimal in terms of positioning accuracy since they include two disjoint stages: i) estimation of initial parameters such as direction, time, or time-difference of arrival (DOA, TOA, TDOA), and ii) localization performed by intersecting the loci provided by initial parameters. Moreover, most of these methods can localize only one signal transmitter. In contrast, the considered DPD is a single-step method which processes data from all sensor elements simultaneously and can resolve several (theoretically one less than sensors number) co-channel signal sources in a medical ward. More precisely, the proposed method can concurrently localize a WCE and some beacons attached to the patient’s body, which can supersede the use of micro-electromechanical-systems (MEMS). In addition, the considered DPD can locate some WCEs for multiple patients at the same time. To do this, DPD seeks for the peaks of a spatial profit function (SPF) which is based on the statistical cumulants of the signals observed at the array sensors placed around the medical ward. Here, we propose the classical DPD (CDPD) as a coarse but fast, and the generalized DPD (GDPD) as a fine and high resolution method for an improved localization of WCE. Also, the proposed technique can be easily extended to any other indoor applications, where the dimensions of the whole monitoring area and receiving array are about some carrier wavelengths. The superiority of CDPD over DOA-based WCE localization technique, and GDPD over CDPD is verified through comprehensive numerical analysis. We have also drawn the Cramer-Rao lower bound (CRLB) as a benchmark for performance analysis of GDPD. Moreover, we introduced a tighter lower bound on the localization mean square error (MSE) achieved with multiple group arrays (MGLB), corresponding to CDPD and DOA-based methods, to show the superiority of GDPD over these techniques.

ОЦІНКА ЕФЕКТИВНОСТІ ВИЯВЛЕННЯ НЕОДНОРІДНОСТЕЙ НА ЗОБРАЖЕННІ ЗА ДОПОМОГОЮ МАКСИМУМА БІАМПЛІТУДИ

The subject of the article is to analyze the effectiveness of a new method for detecting heterogeneities in a digital image by estimating the bimagnitude maximum of the pixel intensities. The aim is to evaluate the effectiveness of the new method of detecting heterogeneities in the image using the maximum of the bimagnitude compared to the known method based on the estimation of the local root mean square deviation (LRMSD) of pixel intensity values. The objectives of the paper are the following: to formalize the procedure for computing the bimagnitude maximum of the pixels in the local segment; create a test image with different contrast values on the borders; to develop a mathematical model for calculating in the Matlab system the efficiency of detecting heterogeneities in the image in the presence of additive Gaussian noise with different values of noise RMS; provide for analysis and comparison of the graphs the receiver operating characteristic (ROC) contained the number of correctly classified non-homogeneous areas versus the number of incorrectly classified areas. The used methods are the following: bispectral data analysis method; methods of probability theory and mathematical statistics; methods of digital image processing. The following results were obtained. A boundary map for the test image without distortion and the presence of additive Gaussian noise with a variance equal to 0.2 is constructed for two types of detectors: the first one is based on the maximum amplitude and the second one is based on the estimation of the local RMS. The results of computer simulations show that both detectors fine-tune the boundary for the images in the absence of noise. But in the presence of additive noise, the detector based on the biamplitude maximum provides a significant advantage. Graphs of the dependence of the number of correctly classified inhomogeneous sections on the number of incorrectly classified areas for the proposed and known reference detection methods are represented. The area under the curve (AUC) values that characterize the efficiency of detecting heterogeneities in the image are calculated. The scientific novelty of the obtained results is the following: a new approach of detecting inhomogeneities in the image is proposed with the help of a new informative feature estimated in the form of the local biamplitude maximum. To analyze the effectiveness of the proposed method, a test image was formed with different border contrast values. Using the proposed technique and the known method, boundary maps were constructed for the test image without distortion and in the presence of additive Gaussian noise. To evaluate the effectiveness of two methods, the graphs were plotted against the number of correctly classified inhomogeneous sites by the number of incorrectly classified (ROC) for both proposed and known detection methods. A detector based on the local RMS value is more effective at small Gaussian noise variance values, but as the noise variance increases, detector based on the biamplitude maximum estimation is more effective. The calculated AUC values for studied methods based on local RMS estimation and maximum biamplitude estimation are equal to 0. 678 and 0. 8468, respectively. Even though the proposed method loses efficiency, the bispectrum-based method is more effective at large values of noise variance, in particular, when the noise RMS is 0.6, AUC = 0.8748.

An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error

Surrogate models are often used as surrogates for computationally intensive simulations. And there are a variety of surrogate models which are widely used in aerospace engineering–related investigation and design. In general, there is an optimal individual surrogate for a certain research object. However, the behavior of an individual surrogate is unknown in advance. Building an ensemble of surrogates by combining different individual surrogates into a weighted-sum formulation is an efficient method to enhance the accuracy and robustness of the surrogate model. Motivated by the previous researches on the ensemble of surrogates, we propose an optimal pointwise weighted ensemble (OPWE) method, wherein the optimal pointwise weight factors are obtained based on the minimization of the local mean square error which is constructed by the global-local error (GLE). By using six well-known mathematical problems and four engineering problems, it is proved that the OPWE proposed in this paper is better than the other ensembles of surrogates in terms of both accuracy and robustness.

Anisotropic Dynamics of Binary Particles in Confined Geometries.

The diffusion dynamics of colloidal particles in a good solvent confined between two parallel quartz walls have been studied within the framework of dynamical density functional theory. The highly ordered density layers induced by interfacial effects give rise to the oscillating dynamics, resulting in position-dependent structural relaxations and diffusivities. Further investigation reveals that particle size, particle-wall interaction, and slitpore width play different roles in affecting the oscillating behaviors along different directions. As a result, the theory yields the local mean square displacements in perpendicular and parallel directions, which agree remarkably well with prior experimental measurements. The results indicate that the mean square displacements can be quantitatively predicted based on the knowledge of inhomogeneous thermodynamics and dynamics. The local and averaged free energy evolution of the binary particles has been described and presented to understand their dynamic mechanism in confined geometry.

Analysis of goodness-of-fit method based on local property of statistical model for airborne sea clutter data

Distribution models are of practical significance to clutter modelling, target detection method and performance evaluation in sea clutter background. The goodness of fit (GOF) between the real sea clutter data and the assumed distribution can be used to choose the proper distribution model. So the great importance of determining the suited statistical model and analysis of goodness-of-fit are highlighted. This paper concentrates on the statistical properties of real L-band airborne sea clutter datasets, which is collected at different grazing angles (30°∼60°). To overcome the shortage of traditional GOF methods, such as Mean Square Difference (MSD) and Modified Mean Square Difference (MMSD), the Variable-window local Mean Square Difference (VW-LMSD) is proposed. By this method, rectangle windows, whose number and position are flexibly selected according to the radar acquirement and sea state, are added to the sea clutter amplitude series to observe the partial GOF results of the statistical distribution models in detail, which provides a multi-angle GOF evaluation to judge the suitable statistical model and establishes a better foundation for clutter modelling, clutter classification and detection criterion selection further.