Jose Carlos Nieto-Borge

Sea clutter reduction and target enhancement by neural networks in a marine radar system.

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.

Estimate of significant wave height from non- coherent marine radar images by multilayer perceptrons

One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height (Hs ). The estimate of Hs 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 Hs . 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.

Nautical Radar Measurements in Europe: Applications of WaMos II as a Sensor for Sea State, Current and Bathymetry

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.

Artificial neural network-based clutter reduction systems for ship size estimation in maritime radars

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.

Analysis of sea state parameters and ocean currents from temporal sequences of marine radar images of the sea surface

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.

Detection of Ships in Marine Environments by Square Integration Mode and Multilayer Perceptrons

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.

Sea clutter power reduction in radar measurement systems by feedforward multilayer perceptrons with medium input data integration rate

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.

MLP solutions for approximating the Average Likekihood Ratio detector in radar applications

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.

Application of conventional marine radars for measuring ocean wave fields in shallow water conditions

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.

Analysis of the sea clutter structure using temporal sequences of X-band marine radar images

This work analyses the spectral structure of the sea clutter obtained from temporal sequences of radar images of the sea surface. The images were acquired by ordinary marine radars, which work in X-band and horizontal polarization. The study analyses the different contributions to the sea clutter spectrum due to those phenomena, such as ocean waves, speckle due to sea surface roughness, etc. that causes the final clutter image in the radar screen.