Pedro Gomez-del-Hoyo

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

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.

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

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.

Passive Radar Array Processing with Non-Uniform Linear Arrays for Ground Target’s Detection and Localization

The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity function domain to exploit integration gain, system resolution and the sparsity of targets in this domain. A modified two-stage detection scheme is described, which is based on a previously-published one by other authors. All of these contributions are validated in a real semiurban scenario, proving the capabilities of detection, the direction of arrival estimation and the tracking of ground targets in the presence of big buildings that generate strong clutter returns. Detection performance is validated through the probability of false alarm and the probability of detection estimation with specified estimation errors.

Passive radars as low environmental impact solutions for smart cities traffic monitoring

Passive radar systems are proposed as ground traffic control and monitoring tools in modern urban areas. These radars use communication networks transmitters available in the area of interest, instead of dedicated ones. The absence of a transmitter reduces dramatically the cost of development, deployment and maintenance, making feasible passive radar sensor networks for covering modern cities complex scenarios. They do not generate electromagnetic compatibility problems, and, more important, they are not associated with health issues. The illuminator selection is one of the most important tasks. The transmitted waveform, the transmitted power, the availability or the coverage areas are critical elements that are out of the control of the radar designer. The IDEPAR demonstrator is used in an urban environment to prove the traffic monitoring capabilities of passive radars exploiting Direct Video Broascasting -Terrestrial transmitters.

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

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.

Artificial intelligence techniques for small boats detection in radar clutter. Real data validation

Abstract Artificial intelligence techniques were applied for detecting small moving targets in maritime clutter environments. Neural detectors are considered to approximate the Neyman–Pearson (NP) in composite hypothesis testing problems. Sub-optimum approaches based on the Constrained Generalized Likelihood Ratio (CGLR) were analysed, and compared to conventional implementations based on Doppler filtering that are designed to filter clutter and improve the Signal-to-Interference Ratio, and Constant False Alarm Rate techniques. The CGLR performance was significantly better at the expense of a high computational cost. As a solution, neural network training sets were designed for approximating the NP detector. The detection of small boats in Gaussian clutter was the defined case study in order to assume the design hypothesis of the conventional solutions and to study their performance under their most favourable conditions. Detection schemes were evaluated using real radar data. Neural solutions based on Second Order Neural Networks provide the best results, being able to approximate the CGLR with a significantly low computational cost compatible with real-time operations.

Coherent VI-MLP-CFAR detector for IDEPAR demonstrator (DVB-T passive radar) in ground traffic monitoring

Coherent CFAR (Constant False Alarm Rate) detectors were designed and evaluated in non-homogeneous DVB-T (Digital Video Broadcasting-Terrestrial) passive radar scenarios. Conventional CFAR techniques are usually applied to noncoherent detector that can not be optimal attending to Neyman-Pearson criterion. The capability to approximate function of neural networks (NN) was used to maintain the false alarm rate without a priori knowledge of the optimum detector outputs. In addition, variability index (VI) was used to provide robustness against non-homogeneous interference. The considered detection scheme was evaluated with real passive radar data acquired by IDEPAR demonstrator, a DVB-T passive radar system, in a terrestrial scenario. Results confirm the potentiality of the coherent VI-MLP-CFAR detector as a low-cost solution for traffic monitoring.

Nondesired effects in DVB-T based passive radar due to sporadic interference

This paper tackles the study of the nondesired effects in the detection capabilities and system resolution of Passive Radar (PR) solutions due to sporadic interference. Real radar data acquired by IDEPAR demonstrator (DVB-T based PR system developed by the University of Alcala) with the presence of an interference were considered. The ambiguity function is calculated to estimate the pedestal mean level and the equivalent information bandwidth. A degradation of the range resolution and a reduction of the Signal-to-Noise Ratio are observed. A simulated radar scenario was designed using the acquired data and adding terrestrial targets to obtain a good approximation of a real environment. Although the resulted detection capabilities are reduced, the target trajectories can be identified. This trend can be explained based on the power migration along range dimension associated with the interference bandwidth, generating blind Doppler shifts in the detection stage. Then, these nondesired effects should be taking into account to improve the PR availability against sporadic interferences.

Study of the ghost target phenomenon on a real DVB-T passive radar scenario

Passive radars are emerging technologies that use non-cooperative transmitters instead of dedicated ones. They present a set of advantages with respect to active ones, such as lower cost and low probability of intercept, but require complex signal processing stages due to the use of non-controlled transmitters. IDEPAR demonstrator is a DVB-T based passive radar developed by the University of Alcalá. DVB-T signals were selected due to their proved good features. Among them, the high availability of transmitters is of great value, but it is associated with the ghost target phenomenon in single frequency networks. In urban and semi-urban scenarios, strong multipaths generated by buildings can also generate phantom targets. In this paper, a study about ghost targets sources on a real scenario is carried out, using real and simulated data. Results prove the big impact of multipaths generated by buildings, concluding that a preliminary study of the presence of these elements in a radar scenario is as important as the analysis of the potential illuminators, specially in urban and semi-urban scenarios, which are of high interest in passive radar applications.