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This paper focuses on modeling a disordered system of quantum dots (QDs) by using complex networks with spatial and physical-based constraints. The first constraint is that, although QDs (=nodes) are randomly distributed in a metric space, they have to fulfill the condition that there is a minimum inter-dot distance that cannot be violated (to minimize electron localization). The second constraint arises from our process of weighted link formation, which is consistent with the laws of quantum physics and statistics: it not only takes into account the overlap integrals but also Boltzmann factors to include the fact that an electron can hop from one QD to another with a different energy level. Boltzmann factors and coherence naturally arise from the Lindblad master equation. The weighted adjacency matrix leads to a Laplacian matrix and a time evolution operator that allows the computation of the electron probability distribution and quantum transport efficiency. The results suggest that there is an optimal inter-dot distance that helps reduce electron localization in QD clusters and make the wave function better extended. As a potential application, we provide recommendations for improving QD intermediate-band solar cells.

This paper focuses on modeling a disorder ensemble of quantum dots (QDs) as a special kind of Random Geometric Graphs (RGG) with weighted links. We compute any link weight as the overlap integral (or electron probability amplitude) between the QDs (=nodes) involved. This naturally leads to a weighted adjacency matrix, a Laplacian matrix, and a time evolution operator that have meaning in Quantum Mechanics. The model prohibits the existence of long-range links (shortcuts) between distant nodes because the electron cannot tunnel between two QDs that are too far away in the array. The spatial network generated by the proposed model captures inner properties of the QD system, which cannot be deduced from the simple interactions of their isolated components. It predicts the system quantum state, its time evolution, and the emergence of quantum transport when the network becomes connected.

This paper proposes a regression Genetic Fuzzy System (GFS, FRULER) for a problem of sea wave parameters estimation from neighbor buoys, with application on wave energy systems. FRULER is a recently proposed, three-staged algorithm that combines an instance selection method for regression, a multigranularity fuzzy discretization of the input variables and an evolutionary algorithm to generate accurate and simple Takagi-Sugeno-Kant (TSK) fuzzy rules. We have applied FRULER to a real problem of significant wave height and energy flux prediction in one buoy of the West Coast of the USA (California), from values of other two neighbor buoys. In the case of the significant wave height, FRULER is able to obtain a robust prediction with only three rules, which in addition are fully interpretable, since they clearly separate swell situations from wind-sea in the prediction. In both cases, the variables used in the significant wave prediction are completely different and can be identified as relevant for the specific case (swell or wind-sea). In the case of the energy flux prediction, the interpretation of the rules provided by FRULER is more difficult, since eight rules are necessary to obtain the prediction. Even in this case, several rules can be clearly classified as swell predictors, and the rest of the rules describe local wind situation of waves. This study shows that the GFSs are useful tools to obtain robust and interpretable predictions in ocean wave parameter estimation problems.

This paper proposes a novel hybrid approach for feature selection in two different relevant problems for marine energy applications: significant wave height (Hm0) and wave energy flux (P) prediction. Specifically, a hybrid Grouping Genetic Algorithm – Extreme Learning Machine approach (GGA-ELM) is proposed, in such a way that the GGA searches for several subsets of features, and the ELM provides the fitness of the algorithm, by means of its accuracy on Hm0 or P prediction. Since the GGA was specifically created for problems involving a number of groups, the proposed algorithm may be used to evolve different groups of features in parallel, which may improve the performance of the predictions obtained. After the feature selection process with the GGA-ELM, the final results are given by an ELM and also by a Support Vector Machine, both working on the best GGA groups obtained. The performance of the proposed system has been tested in a real problem of Hm0 and P prediction at the Western coast of the USA, obtaining good results.

Significant wave height ( H s ) is a basic parameter in wave characterization, important for different problems in marine activities such as the design and management of vessels, marine structures, and wave energy converters. H s is usually estimated using in-situ sensors, mainly buoys, that record time series of wave elevation information. In this paper we propose a method for H s estimation based on a Support Vector Regression algorithm over non-coherent X-band marine radar images. Results for three different platforms (Fino 1, Ekofisk and Glas Dowr) at different locations of the North Sea and South Africa are presented, showing that the SVR obtains a better result than the existing standard method in H s prediction, within different sea states observed at each location.

Wind-generated wave energy is a renewable energy source that exhibits a huge potential for sustainable growth. The design and deployment of wave energy converters at a given location require the prediction of the amount of available wave energy flux. This and other wave parameters can be estimated by means of Computational Intelligence techniques (Neural, Fuzzy, and Evolutionary Computation). This paper reviews those used in wave energy applications, both in the resource estimation and in the design and control of wave energy converters. In particular, most of the applications of Neural Computation techniques, considered here in a broad sense, focus on the prediction of a variety of wave energy parameters by means of Multilayer Perceptrons and, at a lesser extent, by Support Vector Machines, and Extreme Learning Machines. Fuzzy Computation is also applied to estimate wave parameters and control floating wave energy converter. Evolutionary Computation algorithms are used to estimate parameters and design wave energy collectors. We complete this paper with a case study that illustrates, for the first time to the best of our knowledge, the potential of hybridizing a Coral Reefs Optimization algorithm with an Extreme Learning Machine to tackle the problem of significant wave height reconstruction.

In this paper we propose to apply the Support Vector Regression (SVR) methodology to significant wave height estimation using the shadowing effect, that is visible on the X-band marine radar images of the sea surface due to the presence of high waves. One of the main problems of using sea clutter images is that, for a given sea state conditions, the shadowing effect depends on the radar antenna installation features, such as the angle of incidence. On the other hand, for a given radar antenna location, the shadowing properties depend on the different sea state parameters, like wave periods, and wave lengths. Thus, in this paper we show that SVR can be successfully trained from simulation-based data. We propose a simulation process for X-band marine radar images derived from simulated wave elevation fields using the stochastic wave theory. We show the performance of the SVR in simulation data and how SVR outperforms alternative algorithms such as neural networks. Finally, we show that the simulation process is reliable by applying the SVR methodology trained in the simulation-based data to real measured data, obtaining good prediction results in wave height, which indicates the goodness of our proposal.

In this work, we describe an approach that allows for optimizing the structure of a smart grid (SG) with renewable energy (RE) generation against abnormal conditions (imbalances between generation and consumption, overloads or failures arising from the inherent SG complexity) by combining the complex network (CN) and evolutionary algorithm (EA) concepts. We propose a novel objective function (to be minimized) that combines cost elements, related to the number of electric cables, and several metrics that quantify properties that are beneficial for SGs (energy exchange at the local scale and high robustness and resilience). The optimized SG structure is obtained by applying an EA in which the chromosome that encodes each potential network (or individual) is the upper triangular matrix of its adjacency matrix. This allows for fully tailoring the crossover and mutation operators. We also propose a domain-specific initial population that includes both small-world and random networks, helping the EA converge quickly. The experimental work points out that the proposed method works well and generates the optimum, synthetic, small-world structure that leads to beneficial properties such as improving both the local energy exchange and the robustness. The optimum structure fulfills a balance between moderate cost and robustness against abnormal conditions. Our approach should be considered as an analysis, planning and decision-making tool to gain insight into smart grid structures so that the low level detailed design is carried out by using electrical engineering techniques.

At the Helmholtz Zentrum Geesthacht (HZG), Germany an oceanic monitoring system, based on a Dopplerized microwave radar was developed. Within this paper we present the capabilities of this system to measure ocean surface wave properties such as significant wave height, peak wave period, peak wave lengths as well as other spectral wave parameters. In addition, the system is able to detect wave breaking, shear stress of the wind field, near-surface currents, and the bathymetry in shallow water areas. Because of the diversity of measurement capabilities, the system particularly well suited for monitoring and investigation of hydrographic and oceanographic process.

This work analyses wave groupiness features in the three-dimensional spatio-temporal domain by using sea surface information acquired by common X-band marine radars. The analysis is based on the study of the properties of the local and instantaneous envelope derived from wave elevation fields estimated from time series of radar images of the sea surface. The consistence of the analysis techniques proposed in this work are compared with standard one-dimensional wave grouping methods, such as the temporal correlation between consecutive wave heights. These comparisons are carried out using simulations of sea surfaces based on the stochastic description of sea states.

In this paper we propose an approach for feature selection in a problem of significant wave height prediction, to improve the exploitation of marine energy. The method that we present, a Grouping Genetic Algorithm — Extreme Learning Machine approach (GGA-ELM), mainly tries to improve the prediction performance of the regressors, providing more effective predictors and good performance in the final significant wave height prediction. In this method, the GGA looks for several subsets of features, and the ELM provides the fitness of the algorithm, through its accuracy on significant wave height prediction. The GGA is able to evolve different groups of features in parallel, which may improve the performance of the prediction obtained. After the feature selection process with the GGA-ELM, the final results are obtained by applying an ELM and also by a Support Vector Regressor algorithm, both working on the best GGA groups of features previously evolved. In the experimental part of the paper, we show the performance of the proposed approach in a real problem of significant wave height prediction at the West Coast of the USA, using variables directly obtained from several measuring buoys.

A novel method for detecting ships in marine environments is presented in this paper. For this purpose, the information contained in the marine images obtained by a measuring and monitoring marine system is used. The ship detection is done by multilayer perceptrons (MLPs). In the first approach, the MLP processes the information extracted from the images using horizontal or vertical integration modes. However, if a suitable combination of these integration modes is done, better detection performances are achieved. Therefore, the use of an improved integration mode is proposed, which is based on a square shape. These modes are also used in a commonly used detector, the cell averaging constant false alarm rate (CA-CFAR) detector, which is taken as reference in our experiments. The comparison of the performances of both detectors shows how the MLP-based detector outperforms the CA-CFAR detector in all the cases under study. This comparison is based on objective (probabilities of false alarm and detection) and subjective estimations of their performances. The MLP-based detector also presents another advantage, particularly when the square integration mode is considered: high-performance robustness against changes in the marine environmental conditions.

This work compares the spectral analysis of ocean wave fields performed from nautical radar and waverider buoy measurements. The data were taken by a radar station located in the northern coast of Spain and by a buoy that was moored close to the area covered by the radar for its calibration. In this area the sea states are mainly dominated by the presence of northwest swells coming from storms originating in the North Atlantic Ocean. In this order, the radar calibration and the comparisons of different spectral parameters and scalar spectra from both sensors are presented.

Nautical radar and scalar buoy measurements of ocean wind generated waves have been analysed to compare the spectral parameters estimated from both sensors. The time series of different sea-state parameters and the differences and ratios of the values obtained from radar and buoy data using different analysis methods are compared. It has been observed that main differences between the sea-state parameters derived by using measurements obtained from both sensors result both from device characteristics and from the method of spectral estimation. In particular, it is shown that the Nyquist frequency has an important effect on the value of the sea-state parameters depending on spectral moments of order higher than zero.

The objective of the EuroROSE (European Radar Ocean Sensing) project was to combine area covering ground-based remote-sensed wave and current data with high-resolution numerical forecast models to provide nowcasts and forecasts for coastal marine operators. Two experiments to test and to demonstrate the system took place: one on the coast of Norway, north of Bergen in March 2000 and the second on the north coast of Spain at Gijon in October–November 2000. Qualitative and quantitative intercomparisons of the wave measurements and wave model products from these experiments are presented. These include measurements using the Wellen Radar (WERA) high-frequency (HF) radar, the WaMoS (Wave Monitoring System) X-band radar, a directional Waverider and output from the WAM wave model. Comparisons are made of the full directional spectra and of various derived parameters. This is the first-ever intercomparison between HF and X-band radar wave measurements and between either of these and WAM. It has provided a data set covering a much wider range of storm and swell conditions than had been available previously for radar wave-measurement validation purposes and has clarified a number of limitations of the radars as well as providing a lot of very useful radar wave data for future model-validation applications. The intercomparison has led to improvements in the data quality control procedures of both WaMoS and WERA. The two radar sytems measured significant wave height with mean biases of 3% and 6%, respectively, and mean direction differences of less than 2° in both cases. Limitations in the WAM model implementation are also discussed.

With the recent advancement of spatial measurements of ocean waves, we are clearly facing new challenges regarding how to handle an expanded new data system when it becomes widely available. In this paper we wish to present a preliminary attempt at confronting these prospects. Because the data is still very limited at present and also conceptually new, it’s a new, unfamiliar, and unrelenting world to pursue. We need a paradigm shift away from our familiar single-point conceptualization in order to effective approach the new world of truly spatial ocean waves.

The aim of this work is to present an approach to describe complex sea states including the ones consisting of superpositions of swell and wind sea components, using a nautical radar in X-band as a remote sensing technique. In the present article, the inversion method to obtain the spectral representation of the wave fields is described. The method is applied to analyse data obtained from simulation techniques, as well as from measurements obtained during oceanographic campaigns in the Bay of Biscay and North Sea.

This work studies wave groupiness properties in the spatiotemporal domain of wind-generated waves. For that purpose, analysis of temporal sequences of X-band radar images of the sea surface acquired at grazing incidence conditions is carrying out. The results presented in this work are based on the estimation of the local and instantaneous envelope of the sea surface derived from the radar data. The data shown in the paper were acquired from two different X-band radar stations, one located in the North Sea and the other station in the coast of New Zealand.

One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height (H s ). The estimate of H s from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-to-noise ratio (SNR) to linearly estimate H s . This method has been widely used during the last decade, but it presents some limitations, especially when swell-dominated sea states are present. To overcome these limitations, a new non-linear method incorporating additional sea state information is proposed in this article. This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). The information incorporated in the proposed MLP-based method is given by the wave monitoring system (WaMoS II) and concerns not only to the square root of the SNR, as in the standard method, but also to the peak wave length and mean wave period. Results for two different platforms (Ekofisk and FINO 1) placed in different locations of the North Sea are presented to analyze whether the proposed method works regardless of the sea states observed in each location or not. The obtained results empirically demonstrate how the proposed non-linear solution outperforms the standard method regardless of the environmental conditions (platform), maintaining real-time properties.

This work presents the estimation of wave field properties derived from X-band marine radar measurements taken close to coastal locations, where the wave fields are affected by the finite water depth conditions. The work is focused on the detection of individual waves and their related characteristics, such as the estimation of the local and instantaneous wave envelope derived from the wave elevation fields estimated from X-band marine radar time series.

The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs). In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical) integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus) integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.

In radio measurement systems, the backscatter from what is not a target, i.e. the clutter, is usually not desired. So, these systems try to incorporate clutter reduction techniques as efficient as possible. In this way, different signal processing techniques can be used. The case of study presented in this paper shows how to reduce the level of sea clutter measurements in a marine radar. Due to linear linear signal processing is not suitable in these cases, nonlinear signal processing is used, which is achieved by neural networks. In this way, 7 cells to evaluate the output of each cell under test of a radar image (medium input integration rate) are selected. The processed radar images show very promising results from a subjective point of view. On the other hand, objective measurements are used to analyzed the system performance. Those measurements are based on the mean square error and the clutter and target powers at the input and output of the proposed clutter reduction system. Minimum and mean clutter reduction power rates of 7 dB and 10 dB are achieved, respectively.

The presence of sea clutter in marine radar signals is sometimes not desired. So, efficient radar signal processing techniques are needed to reduce it. In this way, nonlinear signal processing techniques based on neural networks (NNs) are used in the proposed clutter reduction system. The developed experiments show promising results characterized by different subjective (visual analysis of the processed radar images) and objective (clutter reduction, target enhancement and signal-to-clutter ratio improvement) criteria. Moreover, a deep study of the NN structure is done, where the low computational cost and the high processing speed of the proposed NN structure are emphasized.

This work presents a method to filter sea clutter features for radar images acquired from ordinary marine radar sensors, which are incoherent radars working in X-band and horizontal polarization. The proposed method considers short temporal sequences of consecutive sea clutter images. This method is based on the acquired experience about the spectral structure of the sea clutter obtained from the use of marine radars to detect ocean wave fields and the related sea state parameters.

A multilayer perceptron (MLP) based detector is proposed for approximating the average likelihood ratio (ALR) detector in composite hypothesis-testing problems. The case of detecting colored gaussian targets with Gaussian autocorrelation function (ACF) and unknown one-lag correlation coefficient (rhos), in colored Gaussian interference with Gaussian ACF is considered. The robustness of the likelihood ratio (LR) detector for known rhos is studied, and and the ALR detector for rhos varying uniformly in [0, 1] is formulated. Due to the complexity of the involved integral, neural network (NN) based solutions are proposed. NNs not only are capable of approximating the ALR detector, but the implemented approximation is expected to have lower computational cost that other numerical approximations, a very important characteristic in real-time applications. MLPs of different sizes have been trained using a quasi-Newton algorithm to minimize the cross-entropy error. Results prove that MLPs with one hidden layer with 17 neurons can implement very robust detectors.

This work uses ordinary X-band marine radars to extract directional wave spectra and their related sea state parameters, as well as speed and direction of ocean surface currents, including tidal information. The used method analyzes the structure in frequency and wave number vector of the image spectra derived from of temporal sequences of marine radar images of the sea surface acquired by a marine radar system. The presented data and the related results were measured from a research platform located in the North Sea. In addition, the work presents some comparisons between sea state parameters derived from the marine radar analysis and the equivalent sea sate parameter obtained from in-situ wave sensor records.

Multilayer perceptron (MLP) based detectors are proposed for detecting Gaussian signals with unknown correlation coefficient (rhos) in additive white Gaussian noise. After proving the low robustness of the likelihood ratio (LR) based detector with respect to rhos, the average likelihood ratio(ALR) based detector assuming a uniform distribution of rhos in [0,1] is formulated. Due to the complexity of the involved integral, two NN based solutions are proposed. MLPs trained with target one-lag correlation coefficient (rhos) varying uniformly in [0,1] outperform the LR-based detector for a fixed rhos, when targets with rhos varying uniformly in [0, 1] are considered for simulation. Two MLPs with 17 hidden neurons each are trained with rhos varying uniformly in [0,b] and [b,1], respectively. Different values of b are studied: 0.25, 0.5 and 0.75. These three detectors outperform the single MLP, and the best results are obtained with b = 0.5 and 0.75. Finally, results show that the number of hidden units of the MLP trained with high values of rhos can be reduced to 8, without reducing the detection capabilities.

This paper presents the remote sensing techniques of measuring sea states, currents and bathymetry by using an X-band nautical radar. It briefly describes the fundamental methods to infer sea state information (e.g. ocean wave and current parameters) from nautical radar imagery. In addition, this work describes in detail the performance of the Wave Monitoring System WaMoS II (a commercial system for real-time monitoring of wave fields based on nautical radar technology). Two examples of nautical radar applications are presented: the first application is an example of the standard WaMoS II installation for sea state measurements, and the second application shows results of a research project aiming at the determination of shallow water bathymetry by means of nautical radar imagery.

This work uses stochastic simulations of linear and nonlinear two-dimensional wave fields to analyze some the statistical properties of the extreme wave crests for a given sea state. The studies carried out consider different spatial resolutions and extensions of the simulated sea state.Copyright © 2008 by ASME

The detection of gaussian signals with unknown correlation coefficient, rhos, is considered. A strategy for designing mixture of experts in composite hypothesis test is proposed. It is based on designing a single multi-layer perceptron (MLP) trained with rhos varying uniformly in [0,1] to approximate the average likelihood ratio (ALR), and evaluate it for fixed values of rhos, so as to identify different variation subintervals of rhos, attending to the single MLP performance. Taking into consideration the relation that exists between MLP structure and the boundaries it is capable to built, we propose to train different MLPs with different sizes for each subinterval (MLP1 and MLP2, for the lower and higher half, respectively) for improving detection capabilities controlling computational cost. To improve the approximation implemented by MLP1, a radial basis function neural network (RBFNN) trained for the lower subinterval of ps has been combined with MLP2. As the functions approximated by the RBFNN and the MLP are equivalent but different, a combination strategy has been proposed based on thresholding the networks outputs and applying them to an OR logic function. Although this scheme does not outperform the 2MLPs, the reduction in computation cost is very important.

The basis functions that are most commonly used in the method of moments (MoM) for the simulation of rough surface scattering have a small support in terms of the wavelength. Complementary, the density of points per wavelength in the surface mesh has to be high enough to guarantee a good accuracy and this implies a computational effort which can often be excessive. Several factors contribute directly to the number of unknowns of the matrix to be inverted in the MoM: the value of the permittivity for the medium below the surface for a dielectric case, its dimensionality and size and the incident or observation angle, depending on the active or passive character of the observables to be calculated. One way of overcoming the difficulty of the exponential growth of unknowns is to use basis functions with a global support over a region larger than a wavelength. If the basis functions are a good representation of the surface fields and currents, a fewer number of terms is needed to represent them than when using a basis of very localized functions. In this work we present an alternative to the classical method of moments with pulse functions and point matching, based on a spectral representation in terms of Bessel functions.

The Integral Equation Model (IEM) was developed originally by Fung and Pan (1986). The assumptions inherent to the model were amended in later publications but there were several important points that remained unclear and that propagated across all subsequent versions of the model. One of the authors of this paper proposed an improved IEM, called IEM2M (Alvarez-Perez, 2001), which removed these unnecessary assumptions and unified the small perturbation model (SPM) and the Kirchhoff approximation (KA) for bistatic scattering. This model was first criticized by Fung (2002) but later adopted by him and co-workers in 2003 and renamed as AIEM (advanced IEM). In contrast to other versions of IEM, IEM2M (integral equation model for second-order multiple scattering) is fully consistent with the SPM. This limit arises naturally from the second-order scattering terms when they produce an extra first-order contribution due to the local non-flatness, and therefore non-Kirchhoff character, of the surface. A central role in the IEM2M is played by a rigorous use of the Weyl representation of the retarded Green function through a homogeneous medium as well as a more physically based selection of the Fresnel coefficients. A further assessment of the model is its implementation for the problem of emissivity calculation. The difference between emissivity from a flat surface and a rough sea-like surface is very small and therefore demands an accurate description of the scattering coefficient, which must be integrated for all angles. Whereas an accuracy of 25% or 1 dB is sufficient for active remote sensing, passive remote sensing calculations require that energy conservation has to be typically within 0.3% error, which is the order of the aforementioned difference for flat and rough, sea-like surface emissivities.

For more than one decade, the scientific developments carried out with spaceborne synthetic aperture radar (SAR) have demonstrated that SAR is a reliable remote sensing technique to analyze wave fields on the open ocean. In their so-called image mode, these SAR systems, such the Advanced SAR (ASAR) ENVISAT, are able to scan ocean areas of 100 x 100 km 2 and more with spatial resolutions about 20 x 20 m 2 . Those SAR images are able to provide information about the spatial variability of 2D wave fields. This work investigates the potential of ENVISAT ASAR capability to scan the ocean surface with dual polarizations. The relevance of these investigations for different practical applications concerning the analysis of ocean waves is discussed.

[1] In recent years a number of remote sensing techniques capable to provide two-dimensional information of the sea surface have been applied to describe sea states. This information is obtained from the estimation of the directional wave number spectrum. However, the wave number spectrum is not sufficient for a complete description of sea states. The analysis of some phenomena such as wave grouping, detection of individual waves, maximum wave heights, etc., needs a complementary study, which has been carried out historically in the temporal domain from buoy records. In two dimensions (e.g., the spatial domain), the complementary analysis of those phenomena can be carried out in the spatial domain rather than the temporal domain. In this work, two methods to analyze wave groupiness in the spatial domain from sea surface images are presented: The first method is based on the two-dimensional generalization of the Smoothed Instantaneous Wave Energy History (SIWEH) function, which had been originally developed to analyze wave groupiness for wave elevation time series. The second method uses the estimation of the wave envelope in two dimensions to extract wave grouping parameters. Assuming that the wave fields are Gaussian stochastic processes, both methods are applied to ERS-2 synthetic aperture radar (SAR) and ENVISAT advanced SAR (ASAR) images of the sea surface, where only cases with almost range traveling waves are considered in order to avoid strong nonlinearities in the SAR imaging mechanisms. The obtained results shown that spaceborne SAR can be used to extract wave grouping information on global scale.

Spaceborne Synthetic Aperture radar is still the only instrument providing continuous wave measurements on a global basis. For more than a decade, the European satellites ERS-1 and ERS-2 have acquired about 1500 globally distributed SAR images daily. It is well known however that strong uncertainties exist in the SAR ocean wave imaging models used so far, which are either purely theoretical or semi-empirical. In this study, a new empirical model for the SAR ocean wave imaging process is proposed. The empirical model is derived based on a global data set of ERS-2 wave mode spectra and collocated two-dimensional ocean wave spectra from the numerical ocean wave model WAM. A quasi-linear model function is fitted using a least-square minimization approach to calculate an optimal estimate of the ocean to SAR transfer function. The empirical transfer function is compared to the theoretical expression used in the literature. The quality of the empirical model is tested by comparison of simulated and observed SAR spectra. Furthermore, the model is used to estimate significant wave heights based on a quasi-linear inversion approach. Statistics as well as global maps of wave parameters are presented. The study is a contribution to the optimization of the operational use of global SAR data for the assimilation of numerical wave forecast models

Recent techniques to derive sea state parameters in the spatial domain from radar images of the sea surface have been derived. This work describes these techniques in order to derive wave elevation maps from space borne SAR and nautical radar images of the sea surface. These wave elevation maps permits to extract additional sea state information in the spatial domain derived from the statistical analysis of the wave elevation, as well as wave height, spatial distribution.

The objective of the EuroROSE project was to combine area covering ground-based remote-sensed wave and current data with high resolution numerical forecast models to provide now- and forecasts for coastal marine operators. The first experiment to test and demonstrate the system took place on the coast of Norway, north of Bergen in March 2000. Qualitative and quantitative intercomparisons of the wave measurements and wave model products from this experiment are presented. These include measurements using the WERA HF radar, the WaMoS X-band radar, a directional waverider and output from the WAM wave model. Comparisons are made of the full directional spectra and of various derived parameters. This is the first ever intercomparison between HF and X-band radar wave measurements and between either of these and WAM.

This study presents a novel way for detecting ships in sea clutter. For this purpose, the information contained in the Radar images obtained by an incoherent X-band maritime Radar is used. The ship detection is solved by feedforward artificial neural networks, such as the multilayer perceptrons (MLPs). In a first approach, the MLP processes the information extracted from the Radar images using the commonly used horizontal and vertical selection templates. But, if a suitable combination of these selection templates is done, better detection performances are achieved. So, two improved selection templates are proposed, which are based on cross and plus shapes. All these templates are also applied in a commonly used detector taken as reference, the CA-CFAR detector. Its performance is compared with the one achieved by the proposed detector. This comparison shows how the MLP-based detector outperforms the CA-CFAR detector in all the cases under study. The results are presented in terms of objective (probabilities of false alarm and detection) and subjective estimations of their performances. The improved MLP-based detector also presents low computational cost and high robustness in its performance against changes in the sea conditions and ship properties.

The European Radar Ocean Sensing (EuroROSE) project is presented. The project was designed to monitor and forecast sea state conditions in coastal areas, in particular port approaches. The aim was to provide area-covering information to traffic managers and search and rescue organizations, using ground based radar and numerical models in combination with data assimilation techniques.

In this study a global data set of reprocessed synthetic aperture radar (SAR) data acquired by the European Remote Sensing satellite ERS-2 is used to study ocean wave grouping using wavelet based methods. For more than a decade the ERS-1/2 satellites have continuously recorded SAR images of the ocean surface. Operating in wave mode both instruments have acquired about 1400 imagettes of 10/spl times/5 km. size (every 200 km along the orbit) each day, which allows to study ocean waves on a global basis. Only coarsely gridded SAR image spectra are available as official wave mode products from the European Space Agency (ESA). As the full image information is required for the present study about 3 weeks of ERS-2 SAR wave mode raw data were reprocessed to 34000 complex SAR images using the BSAR processor from the German Aerospace Center (DLR). ENVISAT satellite, which was successfully launched on February 28, 2002, will provide almost 3000 imagettes a day due to its higher sampling rate (every 100 km). Applying a wavelet edge detection method on the SAR-amplitude-density image and using a region growing approach for the edgefree areas allows examinations of the wave groupiness on a single image. These examinations include group size and number of large groups. The wavelet coefficient as a measure for edge strength is correlated to both wave height and steepness. The wavelet method is compared with an alternative approach, which is based on the classical Hilbert-transform technique. For the latter method the actual sea surface elevation field has to be known. Therefore a quasilinear inversion scheme is used which estimates the surface elevation from complex SAR data.

Space borne synthetic aperture radar are able to provide high resolution measurements of ocean waves on a global scale. The present study uses a reprocessed data set of complex SAR images acquired by the European Remote Sensing satellite ERS-2 to estimate different wave parameters relevant for ship security. In addition, a new method is presented to derive two dimensional sea surface elevation fields from complex SAR data. The method permits to analyze wave fields in more detail than conventional SAR wave measurement techniques, which only estimate the wave spectrum. The technique provides parameters like maximum to significant wave height ratios, wave steepness, or the probability of wave breaking. Global maps and statistics of the new parameters are presented.