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Dive into the research topics where Jaime Martin-de-Nicolas is active.

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Featured researches published by Jaime Martin-de-Nicolas.


Remote Sensing | 2014

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Jaime Martin-de-Nicolas; María-Pilar Jarabo-Amores; David de la Mata-Moya; Nerea del-Rey-Maestre; Jose-Luis Barcena-Humanes

Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.


Expert Systems With Applications | 2015

MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

David de la Mata-Moya; Nerea del-Rey-Maestre; Víctor M. Peláez-Sánchez; María-Pilar Jarabo-Amores; Jaime Martin-de-Nicolas

Neural Networks based CFAR techniques are proposed in an improved coherent radar detector.A coherent detector using a unique CFAR is compared to the classical bank of CFAR techniques.The filter bank output is statistically analyzed to prove Gaussian CFARs unfeasibility.The proposed neural CFAR can be applied to any clutter distribution or detection strategy.A comparative study is carried out on a simulated scenario with complex target trajectories. This paper tackles the detection of radar targets with unknown Doppler shift in presence of clutter. A Neural Network based Constant False Alarm Rate (CFAR) technique is proposed for adapting the detection threshold in an improved architecture based on the Generalized Likelihood Ratio (GLR) detector. Detection schemes based on Doppler processors (Moving Target Indicator (MTI) and Moving Target Detector (MTD)) and conventional CFAR detectors are considered as reference. In these reference solutions, interference is assumed Gaussian and white at the output of each Doppler filter, so conventional incoherent CFAR detectors are applied. The outputs of the CFAR detectors are combined using an OR operation to decide the presence of a target if, at least, one of the CFARs declares it. As a result, the P FA is higher than the desired one, as we prove. In this paper, an improved detector is presented that combines the following features: a better approximation to the Neyman-Pearson detector based on the GLR (selecting the maximum filter bank output), and a unique CFAR detector applied to the squared modulus of the maximum filter bank output. Due to the non-linear nature of the maximum function, conventional CFAR detectors are not suitable. A Neural Network CFAR solution is proposed. A general design method is presented. Results prove that the designed CFAR allows the exploitation of the better detection capabilities of the detector based on the maximum function, providing a higher probability of detection while fulfilling the probability of false alarm requirement. The proposed method can be extended to other detection strategies and radar scenarios.


international conference on digital signal processing | 2013

Neural network based solutions for ship detection in SAR images

Jaime Martin-de-Nicolas; David de la Mata-Moya; María-Pilar Jarabo-Amores; Nerea del-Rey-Maestre; Jose-Luis Barcena-Humanes

Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. Two ship detectors are proposed in this paper. The first one is a MLP-based detector that uses K-distribution parameters to characterize the sea clutter and the brightness of the pixels to detect ships. The second one is the double parameters model, DPM, proposed in the literature. While the DPM-based detector gives rise to some false alarms, leading to the need of a discrimination stage, and has some troubles when the ships are in rough water, the MLP-based detector along with the combination of both sea and ship features obtains better results in terms of detection and false alarm rates.


conference on computer as a tool | 2015

Robustness of a Generalized Gamma CFAR ship detector applied to TerraSAR-X and Sentinel-1 images

Jaime Martin-de-Nicolas; P. Jarabo-Amores; Nerea del-Rey-Maestre; Pedro Gomez-del-Hoyo; Jose-Luis Barcena-Humanes

A fast CFAR ship detector based on the statistical modeling of sea clutter in SAR images is proposed. Typical CFAR detectors, like the double parameter model (DPM), assume a Gaussian sea clutter model and usually degrade the image resolution using target windows. The proposed detector works in a pixel-by-pixel fashion, adaptively selecting a decision threshold for every single patch the SAR image is divided into. Therefore, the presence of different-sized ships will not become an issue and they will be better characterized, allowing the extraction of the target information, which could be used for refocusing, feature extraction, modeling and classification, while maintaining the resolution of the SAR image. Sea clutter is studied using different statistical models, with the Generalized Gamma distribution presenting itself as the most suitable one. This model is used to characterize the sea clutter in the proposed detector. In order to showcase the robustness of the proposed detector, images acquired with SAR sensors working in different frequency bands are selected. Ship detections results show a good performance regardless of the sensor, the ship size and the sea state.


2015 Signal Processing Symposium (SPSympo) | 2015

First results on ground targets tracking using UHF passive radars under non line-of-sight conditions

Pedro Gomez-del-Hoyo; Nerea del-Rey-Maestre; David de la Mata-Moya; María-Pilar Jarabo-Amores; Jaime Martin-de-Nicolas

This paper tackles the evaluation of a Kalman filter-based tracker with real data acquired by a Passive Bistatic Radar (PBR) system. The detection of slow moving targets manoeuvres in ground environment is considered. This radar scenario is characterized by shadowed areas with detection losses due to diffraction phenomena associated with trees, buildings, etc. The application of the designed tracker to real data acquired by a PBR system allows us the study of the detection capabilities to identify tracks in line-of-sight and non line-of-sight areas.


international geoscience and remote sensing symposium | 2012

Demonstrator of maritime SAR applications: Automatic ship detection results

P. Jarabo-Amores; María José González-Bonilla; David de la Mata-Moya; Jaime Martin-de-Nicolas; Ángel Palma-Vázquez

Synthetic Aperture Radar systems are powerful observation tools for maritime surveillance applications such as fisheries monitoring or pirates detection and oil slick detection. It is an important problem that has not been completely solved. The Demonstrator of Maritime SAR Applications (DeMSAR) is proposed as an answer to these problems. It is an off-line implementation of a SAR image processor for detecting ships and/or oil slicks that provides two operation modes: an automatic processing mode for robust detection with default processing schemes and associated parameters, and a toolbox mode with different libraries conveying sets of algorithms designed for specific processing stages that can be configured by the user. The automatic processing scheme includes an edge detector, land mask estimation and a double parameter CFAR ship detector.


international conference on image processing | 2015

A non-parametric CFAR detector based on SAR sea clutter statistical modeling

Jaime Martin-de-Nicolas; P. Jarabo-Amores; N. Rey-Maestre; David de la Mata-Moya; Jose-Luis Barcena-Humanes

A fast non-parametric CFAR ship detector based on the statistical modeling of sea clutter in SAR images is proposed. Typical CFAR detectors, like the double parameter model (DPM), assume a Gaussian sea clutter model and usually degrade the image resolution using target windows. The proposed detector works in a pixel-by-pixel fashion, adaptively selecting a decision threshold for every single patch the SAR image is divided into. Sea clutter is modeled using the Generalized Gamma distribution. Therefore, the presence of different-sized ships will not become an issue and they will be better characterized, allowing the extraction of the target information, which could be used for refocusing, feature extraction, modeling and classification, while maintaining the resolution of the SAR image.


instrumentation and measurement technology conference | 2015

An adaptive threshold technique for the LR detector in K-clutter. Validation using IPIX radar

David de la Mata-Moya; Nerea del-Rey-Maestre; María-Pilar Jarabo-Amores; Jaime Martin-de-Nicolas; Jose-Luis Barcena-Humanes

This paper tackles the detection of radar targets in presence of K-distributed clutter. A detection scheme composed by a preprocessing stage and a likelihood ratio test is proposed for adapting the detection threshold in order to maintain a constant false alarm probability. The conventional Cell-Averaging Constant False Alarm Rate (CA-CFAR) detector is considered as reference one. CA-CFAR technique uses output of the square law detector which cannot be the optimum one. The proposed detection scheme allows the use of a fixed threshold based on the finite number of clutter samples of the reference window. Considered detectors are evaluated with real data acquired by IPIX radar. Results prove that the designed adaptive threshold technique based on the likelihood ratio presents better detection capabilities than CA-CFAR, maintaining for both schemes a similar constant false alarm probability.


conference on computer as a tool | 2015

MLP-based approximation to the Neyman Pearson detector in a terrestrial passive bistatic radar scenario

Nerea del-Rey-Maestre; David de la Mata-Moya; P. Jarabo-Amores; Jaime Martin-de-Nicolas; Pedro Gomez-del-Hoyo

In this paper, the design of Neural Network (NN) based solutions for detecting ground targets using passive radar systems exploiting Digital Video Broadcasting transmitters as illuminators of opportunity, is tackled. Real radar data acquired by a technological demonstrator developed at the University of Alcala was used, to determine suitable statistical models of the interference. To exploit the expected NN based detector performance improvement, a novel approach was proposed to define the observation space for the detection problem. Observation vectors composed of complex radar returns belonging to different Coherent Processing Intervals (CPIs) were considered. For CPIs of 250ms, statistical analyses showed that the problem was an example of detection of Swerling II targets in white Gaussian interference. NN based detectors were designed for approximating the Likelihood Ratio detector (Neyman-Pearson solution). Results were a new prove of NN approximation capabilities, which could be exploited in other passive bistatic radar scenarios.


computational intelligence communication systems and networks | 2013

Doppler Processors as Suboptimum Approaches for Detecting Targets with Unknown Doppler Shift

Nerea del-Rey-Maestre; David de la Mata-Moya; María-Pilar Jarabo-Amores; Jaime Martin-de-Nicolas; Jose-Luis Barcena-Humanes

This paper tackles the detection of Swerling I targets with unknown Doppler shift in colored Gaussian interference. If the radar illuminated area is enough large, a Gaussian clutter model can be assumed. The average likelihood ratio has been formulated and sub-optimum approaches based on the Constrained Generalized Likelihood Ratio (CGLR) analyzed. CGLR detectors have been considered as references solution for evaluating the detection performances and the computational cost of detection schemes including robust Doppler processor based on Moving Target Indicator (MTI) filters. The considered detection schemes are based on a Doppler filtering techniques to reduce the presence of clutter and an envelope detector. Two Doppler processor schemes have been considered: MTI filter designed for maximizing the improvement MTI factor averaged over all target Doppler shifts and a bank of MTI filters optimized for a set of target Doppler shifts. Results show that MTI filter-bank outperforms MTI averaged filter over a wider region of the Ωs variation interval reducing the computational cost with respect to the CGLR detector.

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