R. Vicen-Bueno

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.

Spatial-Range Mean-Shift Filtering and Segmentation Applied to SAR Images

The mean-shift (MS) algorithm is applied for reducing speckle noise and segmenting synthetic aperture radar (SAR) images. Two coastal images acquired by Envisats advanced SAR (ASAR) [European Space Agency (ESA)] are used. Studies of the MS parameters are carried out according to the desired product: a speckle filtered image where textures and edges are preserved, or a segmented image, where land and sea are distinguished, as a previous stage for obtaining a land mask and detecting the coastal line. In all cases, Gaussian kernels are used. Speckle filtering results are compared with those obtained using uniform kernels, proving that the former provides better results than the latter. A segmentation approach based on the positions and frequencies at which the MS converges is applied. The use of a combined spatial-range processing and the corresponding bandwidths makes the MS suitable for the two proposed problems. The solid theoretical basis of this procedure allows designing a guided search of the best parameters according to the desired solution, avoiding a tedious trial-and-error process. Although the used images have different characteristics, results prove that similar sets of parameters can be used, showing some degree of robustness with respect to the image, for a given sensor and image acquisition mode.

Multilayer perceptrons applied to traffic sign recognition tasks

The work presented in this paper suggests a Traffic Sign Recognition (TSR) system whose core is based on a Multilayer Perceptron (MLP). A pre-processing of the traffic sign image (blob) is applied before the core. This operation is made to reduce the redundancy contained in the blob, to reduce the computational cost of the core and to improve its performance. For comparison purposes, the performance of the a statistical method like the k-Nearest Neighbour (k-NN) is included. The number of hidden neurons of the MLP is studied to obtain the value that minimizes the total classification error rate. Once obtained the best network size, the results of the experiments with this parameter show that the MLP achieves a total error probability of 3.85%, which is almost the half of the best obtained with the k-NN.

Comparison of Algorithms for Wind Parameters Extraction From Shipborne X-Band Marine Radar Images

In this paper, curve-fitting and intensity-level-selection (ILS)-based algorithms for wind parameter extraction from shipborne X-band nautical radar images are investigated. First, to exclude the rain cases and low-backscatter images, a data quality control process is designed for both algorithms. An additional process is then introduced for the ILS-based method to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. For the low sea states, a dual-curve-fitting is proposed. These wind algorithms are tested using radar images and shipborne anemometer data collected on the east coast of Canada. It is shown that the dual-curve-fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 5.7° and 0.3 m/s, respectively, under sea states with significant wave height lower than 2.30 m. Also, a harmonic function that is least-squares fitted to the selected range distances vector as a function of antenna look direction is applied. Compared with the original ILS-based algorithm, the modified procedure reduces the standard deviation for wind direction and speed by about 4° and 0.2 m/s, respectively. Finally, the performance of these two modified methods are compared.

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.

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.

Modified LMS-Based Feedback-Reduction Subsystems in Digital Hearing Aids Based on WOLA Filter Bank

Digital hearing aids usually suffer from acoustic feedback. This feedback corrupts the speech signal, causes instability, and damages the speech intelligibility. To solve these problems, an acoustic feedback reduction (AFR) subsystem using adaptive algorithms such as the least mean square (LMS) algorithm is needed. Although this algorithm has a reduced computational cost, it is very unstable. To avoid this situation, other AFR subsystems based on modifications of the LMS algorithm are used. Such algorithms are given as follows: 1) normalized LMS (NLMS); 2) filtered-X LMS (FXLMS); and 3) normalized FXLMS (NFXLMS). These algorithms are tested in three digital hearing aid categories: 1) in the ear (ITE); 2) in the canal (ITC); and behind the ear (BTE). The first and second categories under study suffer from great feedback effects due to the short distance between the loudspeaker and the microphone, whereas the third category suffers from these effects due to the high signal level at the hearing aid output; thus, robust AFR subsystems are needed. The added stable gains (ASGs) over the limit gain when AFR subsystems are working in the digital hearing aids are studied for all the categories. The ASG is determined as a tradeoff between two measurements: 1) segmented signal-to-noise ratio (objective measurement) and 2) speech quality (subjective measurement). The results show how the digital hearing aids that work with AFR subsystems adapted with the NLMS or the NFXLMS algorithms can achieve up to 18 dB of increase over the limit gain. After analyzing the results, it is observed that the subjective measurement always limits the achieved ASG, but when the NLMS algorithm is used, it is appreciated that the objective measurement is a good approximation for estimating the maximum achieved ASG. Finally, taking into consideration the hearing aid performances and the computational cost of each AFR subsystem implementation, an AFR subsystem based on the NLMS algorithm to adapt feedback-reduction filters that are 128 coefficients long is proposed.

A hearing aid simulator to test adaptive signal processing algorithms

This paper deals with the description of a hearing aid simulation tool. This tool simulates the real behavior of digital DSP-based hearing aids with the aim of getting a very promising performance, which can be used for further design and research, and for a better fitting of the hearing impaired patient. The main parameters to program are the noise reduction techniques and the compression and feedback reduction algorithms. Also any other configuration is possible due to the access to the simulated signals in the hearing aid. So we can get a very promising performance which can be used for further design and research and for a better fitting of the hearing impaired patient. Results using a multilevel multifrequency hearing aid with real data collected from 18 patients show how the multifrequency compression techniques adapt the normal perceptible sounds to the hearing impaired patient perceiving area.

Approximating the Neyman-Pearson detector for swerling I targets with low complexity neural networks

This paper deals with the application of neural networks to approximate the Neyman-Pearson detector. The detection of Swerling I targets in white gaussian noise is considered. For this case, the optimum detector and the optimum decision boundaries are calculated. Results prove that the optimum detector is independent on TSNR, so, under good training conditions, neural network performance should be independent of it. We have demonstrated that the minimum number of hidden units required for enclosing the optimum decision boundaries is three. This result allows to evaluate the influence of the training algorithm. Results demonstrate that the LM algorithm is capable of finding excellent solutions for MLPs with only 4 hidden units, while the BP algorithm best results are obtained with 32 or more hidden units, and are worse than those obtained with the LM algorithm and 4 hidden units.

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.