Properties Of Sea Clutter Research Articles

Parameter Estimation for Sea Clutter Pareto Distribution Model Based on Variable Interval

The generalized Pareto (GP) distribution model is often used to describe the amplitude statistical feature of sea clutter. Generally, the parameters of GP distribution are estimated by moments estimators. However, when the sea state is high, the appearance of sea spikes will increase the echo of the anomalous scattering units, which leads to a decrease in the parameter estimation accuracy and target detection performance. To improve the parameter estimation accuracy, this paper proposes a novel parameter estimation method based on variable intervals. Considering the local properties of sea clutter, we take a variable interval of the entire sea clutter series for parameter estimation, where the interval position is selected according to the sea state conditions. For contrast, the bipercentile parameter estimation and truncate moment estimation are also introduced. Finally, the experiment based on the real measured X-band sea clutter datasets indicates that the proposed method outperforms the state-of-the-art moments estimators.

Multifractal Correlation Analysis of Autoregressive Spectrum-Based Feature Learning for Target Detection Within Sea Clutter

Fractal theory has improved the target detection performance under sea clutter background. However, the traditional fractal methods in time domain or Fourier domain cannot accurately characterize the complex sea clutter properties, which leads to the degradation of the target detection performance under low signal-clutter-ratio (SCR) conditions. This article investigates the multifractal correlation property of sea clutter in the autoregressive (AR) spectrum domain for detection performance improvement. In this work, the traditional target detection problem is converted to a binary classification problem of sea clutter and targets. The refined fractal characteristics in various singularity scale interval and range bins are analyzed, and the AR singularity intensity correlation function width together with the accumulation area of AR multifractal correlation spectrum is extracted as intrinsic features. Then, a simple and efficient fully connected network is developed to realize the classification. Experimental results on real measured marine radar datasets demonstrate that the proposed method can increase the detection probability by about 15% than the state-of-art fractal-based methods under a low SCR condition.

Modeling and simulation methods of sea clutter based on measured data

The modeling and simulation of sea clutter are important in detecting radar targets in sea backgrounds. Because the nonstationary property of sea clutter is ignored in traditional statistical models, a new method based on measured sea clutter is proposed in this paper. First, we convert the measured sea clutter data under different sea conditions [[Formula: see text]] into real amplitude [Formula: see text]. Instantaneous phase [Formula: see text] is then extracted from the coherent radar’s baseband data. Second, we select a candidate statistic model and estimate its parameters based on [Formula: see text] by utilizing maximum likelihood estimation. Finally, we generate random series [Formula: see text] using corresponding random data generator and then add instantaneous phase [Formula: see text] into [Formula: see text], i.e., [Formula: see text], to obtain simulated sea clutter series. Through a comparison of simulated sea clutter and measured sea clutter data via histogram, the validity of the proposed method is proved.

A Bayesian Angular Superresolution Method With Lognormal Constraint for Sea-Surface Target

Maximum a posteriori (MAP) approach, based on Bayesian criterion, is proposed to overcome the low azimuth resolution in real-aperture imaging. The essence of this approach is to use the statistical characteristics of the imaging background and target to invert the real target scene. This paper presents a deconvolution method based on Maximum a posteriori (MAP) criterion, which combines the Rayleigh distribution and Lognormal distribution, to realize high angular resolution for sea-surface target. Firstly, Rayleigh distribution is considered to express the statistical properties of sea clutter. Moreover, the Lognormal distribution is employed to represent the statistical properties of target as prior information. The reason is that Lognormal distribution can be approximatively regarded as a combined constraint term. Finally, the optimization theory is utilized to obtain the iterative estimated solution. The processed results of simulation and measured data are given to verify the performance of proposed algorithm.

Weak Target Detection Based on Joint Fractal Characteristics of Autoregressive Spectrum in Sea Clutter Background

To overcome the shortcomings of fractal analysis in the time domain and Fourier transform domain, this letter mainly studies the joint fractal property of sea clutter of autoregressive (AR) spectrum and its application on weak target detection. Since the box-counting dimension is the most popular parameter to describe a fractal set and simply to calculate, we combined the box-counting dimension with AR spectrum estimate theory, which considers the correlation property of sea clutter series. Moreover, the intercept is regarded as an auxiliary feature for target detection. Then the box-counting dimension and intercept are used as a 2-D feature to analyze the joint fractal characteristic of AR spectrum, and a novel weak target detection algorithm is proposed based on the joint fractal characteristic of AR spectrum. In fact, radar target detection can be regarded as a binary-classification question, and the support vector machine (SVM) is applied to target detection. Finally, real S-band sea clutter data sets are analyzed. Compared to the traditional CFAR method and existing fractal methods, the proposed method improves the detection performance without complex computations.

Correlation feature-based detector for range distributed target in sea clutter

In this paper, a novel correlation feature-based detector is proposed to deal with the challenging problem of detecting a range-distributed target embedded in nonstationary sea clutter. It is well known that sea clutter consists of a speckle component modulated by texture. The nonstationary property of sea clutter is mainly reflected in texture, but its correlation characteristic is mainly dominated by the speckle. Therefore, this detector using the correlation feature of sea clutter can effectively eliminate the negative effect of the nonstationary property of sea clutter on the detection performance. In addition, in order to get rid of the limitation of the knowledge shortage of target scatterers, the modified entropy method is applied to adaptively estimate the number of target scatterers. The real range distributed target data and high-resolution sea clutter are used to evaluate the detector, and the experimental results show that it attains a better detection performance in comparison with several existing detectors. Comparing with the feature-based detector, the proposed detector can effectively reduce the computational complexity.

Facet-Based Investigation on Microwave Backscattering From Sea Surface With Breaking Waves: Sea Spikes and SAR Imaging

A complete facet model for the backscattering from rough sea surface with breaking waves is proposed in this paper. In consideration of the spatial distribution of breaking waves on sea surface, the model is able to give a good interpretation to the “super events” under high sea states, such as sea spikes and high polarization ratios. Based on the proposed model, normalized radar cross section plots of sea surface under backscattering configuration are calculated and compared with measured data. The comparisons show that the proposed model is tractable to estimate the scattering from electrically large ocean surface under high sea states with accuracy and efficiency. In addition, the non-Gaussian statistics and spatial correlation properties of sea clutter are analyzed under different range resolutions and incident angles. The results show that high kurtosis value, which means a sea spike phenomenon, mostly happens in lower grazing angle and higher range resolution cases. The comparisons of simulated SAR images of sea surface with and without breaking waves also reveal that sea spikes and high polarization ratios are caused by breaking waves. All the simulation results prove that our model not only is able to explain the physical mechanism of the scattering but also can be applied to the analyses of statistical properties of sea clutter under high sea states.

Range Distributed Floating Target Detection in Sea Clutter via Feature-Based Detector

We investigate the detection of range distributed floating target in sea clutter that consists of stationary speckle modulated by nonstationary texture. With the elimination of the nonstationary part of sea clutter, here a feature-based detector using the consistency factor of speckle is proposed. Experimental results of IPIX radar data sets show that the proposed feature-based detector achieves the better detection performance in comparison with other methods. The nonstationary property of sea clutter has a negative effect on the probability of detection, thus warrant consideration in designing the detector.

Factors Affecting the Statistical Properties of Sea Clutter

SUMMARYWe observed sea clutter in the sea area east of the Daini Kaiho sea fort in Tokyo Bay using an X‐band radar, and obtained data from 21,996 cells by 47 consecutive observations. In order to investigate the statistical properties, we performed estimation of parameters by maximum likelihood estimation and a test of goodness of fit by the Akaike Information Criterion (AIC), as models for the Weibull, log‐Weibull, and log‐Normal distributions. The reflected intensity distribution of sea clutter was found to obey the Weibull distribution better than any other distribution, and we therefore made a study using the shape parameter of the Weibull distribution. It was found that sea conditions such as swells and wind waves affect the statistical properties of sea clutter.

Multichannel Analysis and Suppression of Sea Clutter for Airborne Microwave Radar Systems

One efficient method to detect slow targets with low signal-to-clutter-plus-noise ratios is space–time adaptive processing (STAP). To apply STAP to maritime radar systems, the multichannel properties of sea clutter have to be known. This paper investigates these characteristics for airborne microwave radar systems to detect vessels in the open sea. It is shown that the motion of sea echoes and different sea scattering types cause different multichannel properties of sea clutter compared with land clutter. This paper provides a mathematical basis to describe multichannel sea clutter and predict STAP performance over water. To confirm the derived properties, several real multichannel sea data sets for different swell directions and sea states were acquired with the airborne radar system Phased Array Multifunctional Imaging Radar (PAMIR), and simulations were conducted. This paper demonstrates the reproducibility of real sea clutter multichannel characteristics for different sea conditions with the help of calculations and simulations, which are proposed in this analysis.

Numerical Simulation and Analysis of the Spiky Sea Clutter from the Sea Surface With Breaking Waves

This paper presents a feasible and efficient scattering model of the time-varying complex sea surface process to the field of sea spike modeling. To describe the phenomena of sea spikes as well as the “super events” from realistic sea clutter, wedges are posited on the two-scale sea surface to provide an approximate simulation of the breaking waves. The scattering of the wedge and multiple reflections that occur from the sea and wedges are considered in ray tracing for a large HH/VV ratio. With this technique, we obtained the time sequence of the time-varying sea surface and identified the spiking events in the background. Sea clutter properties with breaking waves are compared with that of the sea. Results indicate that spiking events are more likely to occur in the sea with breaking waves. The probability distribution function (PDF) curve of the sea surface with breaking waves has a heavy trail in the large amplitude region. The decorrelation time of the sea with breaking waves is significantly longer compared with the sea without breaking waves, particularly, for HH polarization. The spike properties of sea clutter in different wind speeds and resolutions are also investigated. Results show that spike events are more likely to happen under a high sea state and radar resolution. The Doppler spectrum of the sea with breaking waves showed remarkable improvement for both polarizations. All simulation results indicate that the proposed scattering model effectively stimulates sea clutter with a low grazing incident angle and explains the physical mechanism of the sea spikes.

Fractal properties of autoregressive spectrum and its application on weak target detection in sea clutter background

This study concerns the fractal properties of sea clutter in the power spectrum domain. To overcome the deficiencies of Fourier transform analysis, the power spectrum of the sea clutter is obtained by autoregressive (AR) spectrum estimation. The AR model is a linear predictive model, which estimates the power spectrum of sea clutter form its autocorrelation matrix and has a higher frequency resolution than Fourier analysis. This study concentrates on analysing the fractal property of the power spectrum based on AR spectral estimation and its application on weak target detection. First, fractional Brownian motion is taken as an example to prove the fractal property of the power spectrum. Then, real measured X-band data is used to verify the fractal property of the power spectrum of sea clutter. Finally, a novel detection method based on AR Hurst exponent is proposed and the factors influencing the fractal properties of power spectrum are analysed. The results show that the Hurst exponent of AR spectrum is effective for weak target detection in sea clutter background. Compared with the existing fractal method and the traditional constant false alarm rate (CFAR) method, the proposed method has a better detection performance.

Weak target detection in sea clutter background using local‐multifractal spectrum with adaptive window length

Weak target detection based on fractal analysis is a hot research topic. The existing methods, using single fractal dimension and Hurst exponent, are not applicable under low signal clutter ratio (SCR) conditions. This study focuses on enhancing the performance of weak target detection by utilising the multifractal properties of sea clutter. The existing global multifractal spectrum based method has limited performance because of its non-stability. Therefore this study proposes a weak target detection method by taking into account the local difference of multifractal spectrum between sea clutter with and without target. The local-multifractal spectrum is obtained by adding a rectangle window to the multifractal spectrum, and the local difference is calculated by the local mean square summation algorithm. In addition, the window length, a key parameter for obtaining the local-multifractal spectrum, is adaptively computed by the slope of the singularity intensity. In comparison with the existing multifractal spectrum based method, the experimental results of real S-band and X-band sea clutter data show that the proposed method is more stable, and could achieve a better detection performance under low SCR conditions.

シークラッタの統計的性質に影響を与える要因

We observed sea clutter of sea area in the east of the Daini Kaiho sea fort in Tokyo Bay using an X-band Radar, and we obtained the data of 21,996 cells by observation of consecutive 47 times. In order to investigate the statistical properties, we made estimation of parameters by maximum likelihood estimation and a test of goodness of fit by AIC as models for Weibull, log-Weibull, and log-Normal distributions. As a result, the reflected intensity distribution of sea clutter obeyed Weibull distribution better than any other distributions, and so we made a study using shape parameter of Weibull distribution, it was found that sea condition such as swell and wind wave affect the statistical properties of sea clutter.

Fractal property of sea clutter FRFT spectrum for small target detection

Because the detection of small target in the background of sea clutter is strongly influenced by the sea condition, this paper studies the fractal property of sea clutter in fractional Fourier transform (FRFT) domain and proposes the fractal detection method in single and high dimensions. The FRFT deduced from mathematical definition is not consistent with the self-similar properties in orders and scales. Multifractal detrended fluctuation analysis (MF-DFA) method is used to determine the fractal parameter H(q) and analyze the fractal property of the sea clutter in different situations, distances, and polarizations. In single dimension, the small target detection method is proposed based on an adaptive order. By comparing different factors of the multifractal parameters, the results show that the transform order method in sea clutter FRFT domain can detect small signals under complicated sea conditions. The detection threshold mostly increases above 200%, which is 26.3% higher than the method of time domain signal. H(q) has an obvious multifractal difference on high negative scale, the H(q)-q curve satisfies the arctangent distribution. The fitting amplitude ratios of pure sea clutter and target data are greater than 1.8 (HH) and 1.4 (VV), which provide the basis for the small target detection in sea clutter background.

Targets Detection Based on Multifractal Method

Mean sea clutter scattering coefficient model can reflect the relationship between the scattering coefficient of sea clutter and radar parameters and environmental factors, commonly used at sea clutter radar range forecasts. Sea clutter amplitude distribution model is essentially in the form of a probability density function to describe the magnitude of the statistical properties of sea clutter. In this paper, based on the sea surface target detection process uncertain information based on multiple objective study of fractal theory, simulation results show that our detection methods to get good results, you can improve the reliability and intelligence of detection.

The Fractal Properties of Sea Clutter and Their Applications in Maritime Target Detection

This letter studies the fractal properties of sea clutter, mainly including the box-counting dimensions and multifractal spectra. Those properties of sea clutter with targets and sea clutter without targets are compared and found different, which enables us to develop methods to detect low-observable targets within sea clutter. An integral test is utilized to compare the multifractal spectra of sea clutter under different conditions. In practice, researchers can hardly get enough real data of variable signal-to-clutter ratio (SCR) to get systemic conclusions. Therefore, we construct new sea clutter series from real data with variable SCR. The results show that the detecting methods based on fractal analysis have strong performance of maritime target detection at very low SCR.

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Effective detection of low observable moving target at sea is important for remote sensing and radar signal processing. The non-Gaussian property of sea clutter and lack of accurate model make the detection difficult for statistics based detectors. Also the fractal techniques in time domain cannot achieve high detection probability in heavy sea clutter. To help solve the problems, fractal characteristics of IPIX datasets in fractional Fourier transform (FRFT) domain are analysed making use of the fluctuation of FRFT amplitudes and moving target detection algorithms are proposed based on the fractal characteristics in FRFT domain. Firstly, fractal model in FRFT domain is established with fractional Brownian motion model and two judgment and extraction methods are employed for calculating the fractal characteristics in FRFT domain. It is found that sea clutter of different polarisations exhibit fractal behaviours in FRFT domain, that is, self-similarity property, within its corresponding scale-invariant interval. Then, we find that four specific fractal statistics in the best FRFT domain can provide valuable information for developing simple and effective detectors. Finally, traditional amplitude detector and Hurst exponent detector in time domain are compared and the results prove the superior detection ability of low observable moving target without complex computations.

Study on Effect of Sea Condition for Statistical Properties of Sea Clutter using an X-band Radar

We observed sea clutter using an X-band Radar in some area of Tokyo Bay including ships on the Uraga Suido traffic route and the Daini Kaiho sea fort, and furthermore observed the sea condition. By analyzing the observed data statistically, we studied the effect of the sea condition for the statistical properties of the sea clutter. As a result, we showed that the sea clutter in case of including no target obeyed the Weibull distribution but that in case of including target tended to obey the Log-Weibull distribution or Log-normal distribution. And also we showed that correlation between the shape parameters c of Weibull distribution, Log-Weibull distribution and the significant wave height were strong. In addition, we showed that there was no correlation not only between the wave period and the shape parameter c of Weibull distribution, but also between the wave period and the shape parameter c of Log-Weibull distribution.

Low Observable Targets Detection by Joint Fractal Properties of Sea Clutter: An Experimental Study of IPIX OHGR Datasets

We exploit the joint fractal properties of sea clutter extracted from detrended fluctuation analysis (DFA) for targets detection. We find that two specific fractal statistics, i.e., the intercept at the crucial scale and the Hurst exponent of optimal scales provide valuable information for targets detection. The first statistic measures the discrepancy between sea clutter and low observable targets at the crucial fractal scale, and the second one evaluates the average fractal difference within the optimal multi-scales. A target detection method integrating these two statistics is proposed, which is validated by real-life IPIX radar datasets. We find that this joint fractal detection approach achieves more accurate results for low observable targets detection.