Advanced Sea Clutter Models and their Usefulness for Target Detection

International audienceRobust naval target detection is of significant importance to national security, to navigation safety, and to environmental monitoring. Here we consider the particular case of high resolution coastal radars, working at low grazing angles. The robustness of detection heavily relies on the appropriate knowledge of two classes of backscattered signals: the target echo, and the sea echo. The latter, usually regarded as a noise, is known as the sea clutter. This particular combination, of high resolution and low grazing angles, raises considerable challenges to radar processing algorithms. Specifically, the probability density function governing the sea clutter amplitude is no more Gaussian and a lot of effort has been aimed at characterizing it. Three approaches are reviewed here: the stochastic, texture and chaotic models. While the stochastic models represent an essay to extend classical detection theory to radars operating in marine environment, the other two models represent entirely new paradigms. Since each model has its strengths and weaknesses and more testing on real data is required to credibly validate any of the proposed models, a definitive conclusion is far from reach. However, critical comments, as well as experimentally supported conclusions are presented in the paper

Основные характеристики морского клатера, влияющие на обнаружение малоразмерных малоподвижных целей морскими РЛС

В роботі здійснюється пошук математичної моделі морського клатера, придатної для створення на її основі алгоритму виявлення малорозмірних малорухомих цілей морськими РЛС. В результаті аналізу джерел для моделювання стохастичного розподілу амплітуди морського клатера обирається компонована Гаусова модель, оскільки її адекватність підтверджена найбільшою кількістю дослідників. В якості перспективної альтернативи стохастичній моделі обирається обговорювана в останнє десятиліття в літературі модель, основана на теорії хаосу, перевага використання якої для вирішення даного класу задач потребує остаточного підтвердження або заперечення.Searching of the sea clutter mathematical model is carried out in this paper. It is suitable to create based on it algorithm for small slow moving targets detection by marine radars. The compound Gaussian model for modeling sea clutter amplitude stochastic distribution is selected as a result of the sources analysis, because it was confirmed by most of researches. The discussed in the literature model based on chaos theory is choosen as perspective alternative for stochastic model; its advantage of using it for such problems solution must be definitively proved or denied. It was proposed many different distributions for high resolution sea clutter amplitude data modeling. The most frequently reported in the literature are K, Log-Normal and Weibull distributions. K distribution belonging to a compound-Gaussian model has the most significant theoretical and experimental background. This distribution choice is physically explained basing on the processes taking place when electromagnetic waves scattered from capillarity and gravity sea waves create a composed echo. Signal representing this echo is the product of two random components, called texture and speckle. Texture is the result of scattering from gravity waves, has a Gamma pdf (in case of K distribution) and corresponds to slow-varying large-scale structure. Speckle is the result of scattering from isolated scatterers (capillarity waves), has a Rayleigh pdf and corresponds to rapid varying small-scale structure. So, K distribution envelope is a compound distribution consisting of a locally Rayleigh distribution speckle whose mean is modulated by a gamma distribution texture. All researches consider Rayleigh pdf for speckle. The lognormal, generalized Gaussian, inverse gamma and some other distributions were proposed for the texture. Due to literature analyses it is seen that texture distribution depends on radar range resolution, but strong dependence is not proved. Some scientists modified K distribution to K-A distribution consisting of the Rayleigh, gamma and Poisson distributions to describe better spikes appearence caused by whitecaps and bursts. Using of Weibull-Weibull (WW) and KK distributions was proposed for high grazing angle and high resolution sea clutter. Doppler characteristics of the sea clutter has been investigated by many researchers and now we have well developed theory. It is known empirical behavior of sea clutter doppler spectrum for different conditions – grazing angle, resolution, wind speed, polarisation and others. Lee, Walker and Ward models are used for sea clutter doppler spectrum describing. Fast moving targets can be effectively detected in heavy sea clutter by doppler radars. But existing theory cannot improve detection of slow moving small targets in heavy sea clutter, because slow moving targets have doppler shift compared to doppler shift of sea clutter. Correlation properties of high resolution sea clutter cannot be derived from its doppler spectrum. In alternative to stohastic model, many researches prefer deterministic model and use chaos theory to describe sea clutter. This choise is based on the fact that both hydrodynamic and electromagnetic therory relying on deterministic models only. If deterministic theory usefulness in applying to high resolution see clutter description be proved completely, it can lead to great progress for small targets in heavy sea clutter detection; because in this case sea clutter behavior can be predicted if initial conditions are precisely known. Using chaotic model for high resolution sea clutter description is highly disputed in recent years, and many researches have questioned first results of high resolution sea clutter describing with chaotic theory usage by Haykin. But great possibilities can give deterministic model for small targets detection definitively proving its ability to describe high resolution sea clutter data precisely causes different scientists to return to chaos theory again and again. Promising results in this field was obtained by using multifractal theory, but still there are not strong methodological background of using deterministic models for small slow moving targets in sea clutter detection, so it is required to make research to prove or deny deterministic models usefulness for high resolution sea clutter data description.В работе осуществляется поиск математической модели морского клатера, пригодной для создания на ее основе алгоритма обнаружения малоразмерных малоподвижных целей морскими РЛС. В результате анализа источников для моделирования стохастического распределения амплитуды морского клатера избирается составная Гауссова модель, поскольку ее состоятельность подтверждена наибольшим количеством исследователей. В качестве перспективной альтернативы стохастической модели избирается обсуждаемая в литературе модель, основанная на теории хаоса, преимущество использования которой для решения данного класса задач требует окончательного подтверждения или отрицания

Artificial Intelligence in Marine Science and Engineering

This Special Issue covers research in Artificial Intelligence in Marine Science and Engineering and shows how to apply it to many different professional areas, e [...

The Target Tracking Algorithm Based on Environment Technology

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Nonlinear processing of non-Gaussian stochastic and chaotic deterministic time series

It is often assumed that interference or noise signals are Gaussian stochastic processes. Gaussian noise models are appealing as they usually result in noise suppression algorithms that are simple: i.e. linear and closed form. However, such linear techniques may be sub-optimal when the noise process is either a non-Gaussian stochastic process or a chaotic deterministic process. In the event of encountering such noise processes, improvements in noise suppression, relative to the performance of linear methods, may be achievable using nonlinear signal processing techniques. The application of interest for this thesis is maritime surveillance radar, where the main source of interference, termed sea clutter, is widely accepted to be a non-Gaussian stochastic process at high resolutions and/or at low grazing angles. However, evidence has been presented during the last decade which suggests that sea clutter may be better modelled as a chaotic deterministic process. While the debate over which model is more suitable continues, this thesis investigates whether nonlinear processing techniques can be used to improve the performance of maritime surveillance radar, relative to the performance achievable using linear techniques. Linear and nonlinear prediction of chaotic signals, sea clutter data sets, and stochastic surrogate clutter data sets is carried out. Volterra series filter networks and radial basis function networks are used to implement nonlinear predictors. A novel structure for a forward-backward nonlinear predictor, using a radial basis function network, is presented. Prediction results provide evidence to support the view that sea clutter is better modelled as a stochastic process, rather than as a chaotic process. The clutter data sets are shown to have linear predictor functions. Linear and nonlinear predictors are used as the basis of target detection algorithms. The performance of these predictor-detectors, against backgrounds of sea clutter data and against a background of chaotic noise data is evaluated. The detection results show that linear predictor-detectors perform as well as, or better than, nonlinear predictor-detectors against the non-Gaussian clutter backgrounds considered in this thesis, whilst the reverse is true for a background of chaotic noise. An existing, nonlinear inverse, noise cancellation technique, referred to as Broomhead’s filtering technique in this thesis, is re-investigated using a sine wave corrupted by broadband chaotic noise. It is demonstrated that significant improvements can be obtained using this nonlinear inverse technique, relative to results obtained using linear alternatives, despite recent work which suggested otherwise. A novel bandstop filtering approach is applied to Broomhead’s filtering method, which allows the technique to be applied to the cancellation of signals with a band of interest greater than that of a sine wave. This modified Broomhead filtering technique is shown to cancel broadband chaotic noise from a narrowband Gaussian signal better than alternative linear methods. The modified Broomhead filtering technique is shown to only perform as well as, o

Short-term rainfall nowcasting: using rainfall radar imaging

As one of the most useful sources of quantitative precipitation measurement, rainfall radar analysis can be a very useful focus for research into developing methods for rainfall prediction. Because radar can estimate rainfall distribution over a wide range, it is thus very attractive for weather prediction over a large area. Short lead time rainfall prediction is often needed in meteorological and hydrological applications where accurate prediction of rainfall can help with flood relief, with agriculture and with event planning. A system of short-term rainfall prediction over Ireland using rainfall radar image processing is presented in this paper. As the only input, consecutive rainfall radar images are processed to predict the development of rainfall by means of morphological methods and movement extrapolation. The results of a series of experimental evaluations demonstrate the ability and efficiency of using our rainfall radar imaging in a nowcasting system

Technique-Based Exploitation Of Low Grazing Angle SAR Imagery Of Ship Wakes

The pursuit of the understanding of the effect a ship has on water is a field of study that is several hundreds of years old, accelerated during the years of the industrial revolution where the efficiency of a ship’s engine and hull determined the utility of the burgeoning globally important sea lines of communication. The dawn of radar sensing and electronic computation have expanding this field of study still further where new ground is still being broken. This thesis looks to address a niche area of synthetic aperture radar imagery of ship wakes, specifically the imaging geometry utilising a low grazing angle, where significant non-linear effects are often dominant in the environment. The nuances of the synthetic aperture radar processing techniques compounded with the low grazing angle geometry to produce unusual artefacts within the imagery. It is the understanding of these artefacts that is central to this thesis. A sub-aperture synthetic aperture radar technique is applied to real data alongside coarse modelling of a ship and its wake before finally developing a full hydrodynamic model for a ship’s wake from first principles. The model is validated through comparison with previously developed work. The analysis shows that the resultant artefacts are a culmination of individual synthetic aperture radar anomalies and the reaction of the radar energy to the ambient sea surface and spike events

On the estimation of the correlation dimension and its application to radar reflector discrimination

Recently, system theorists have recognized that low order systems of nonlinear differential equations can give rise to solutions which are neither periodic, constant, nor predictable in steady state, but which are nonetheless bounded and deterministic. This behavior, which was first described in the study of weather systems, has been termed 'chaotic.' Much study of chaotic systems has concentrated on analysis of the systems' phase space attractors. It has been recognized that invariant measures of the attractor possess inherent information about the system. One such measure is the dimension of the attractors. The dimension of a chaotic attractor has been shown to be noninteger, leading to the term 'strange attractor;' the attractor is said to have a fractal structure. The correlation dimension has become one of the most popular measures of dimension. However, many problems have been identified in correlation dimension estimation from time sequences. The most common methods for obtaining the correlation dimension have been least squares curves fitting to find the slope of the correlation integral and the Takens Estimator. However, these estimates show unacceptable sensitivity to the upper limit on the distance chosen. Here, a new method is proposed which is shown to be rather insensitive to the upper limit and to perform in a very stable manner, at least in the absence of noise. The correlation dimension is also shown to be an effective discriminant in distinguishing between radar returns resulting from weather and those from the ground. The weather returns are shown to have a correlation dimension generally between 2.0 and 3.0, while ground returns have a correlation dimension exceeding 3.0