Article
Environmental Sciences
Liang Zhang, Zhijun Zhang, Shengtao Lu, Deliang Xiang, Yi Su
Summary: This paper proposes a new superpixel-based non-window CFAR ship detection method for SAR images, which resolves the problems of CFAR detection being affected by speckle noise and having a high computation load caused by the sliding window technique. Experimental results show that the proposed method achieves ship detection with higher speed and accuracy compared to other state-of-the-art methods.
Article
Environmental Sciences
Zhenyu Li, Jianlai Chen, Yi Xiong, Hanwen Yu, Huaigen Zhang, Bing Gao
Summary: A hierarchical scheme of ship detection, imaging, and classification is proposed in this paper, which utilizes a single-channel synthetic aperture radar (SAR) mounted on maneuvering rotor platforms. The scheme involves ship prescreening using an adaptive background window model and discrimination based on micro-Doppler motion properties, which is validated through simulation and field data processing.
Article
Geochemistry & Geophysics
Juha Karvonen, Alexandru Gegiuc, Tuomas Niskanen, Anni Montonen, Jorgen Buus-Hinkler, Eero Rinne
Summary: A new unsupervised method for iceberg detection over sea ice-free waters is proposed in this study, which shows a reduced number of false alarms compared to existing algorithms while effectively detecting icebergs. The algorithm is based on the segmentation and nonparametric constant false alarm rate (SnP-CFAR) approach.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Marine
Jian Wang, Haisen Li, Guanying Huo, Chao Li, Yuhang Wei
Summary: In the background of multi-background underwater surveying and mapping, detecting seafloor terrain is challenging due to environmental noise, sidelobe data, and tunnel emission. Constant false alarm detection, which can eliminate noise interference and provide accurate seabed topography information, is an important research field. This paper proposes an efficient weighted cell averaged constant false alarm detection method (WCA-CFAR) to increase detection probability, reduce missing probability, and improve detection speed. The method is validated through simulation data detection tests and actual lake test data, showing effective reduction in missing detection probability and improvement in detection probability.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Civil
Yiping Xie, Nils Bore, John Folkesson
Summary: In this article, a novel data-driven approach for high-resolution bathymetric reconstruction from sidescan is proposed. The full bathymetry is estimated by combining the coarse seabed bathymetry from the navigation system with the high-resolution seabed slope information from the sidescan. A fully convolutional network is used to estimate the depth contour and its uncertainty from the sidescan images and sparse depth. After fusing the depth predictions and confidence measures, a high-quality bathymetric map is reconstructed.
IEEE JOURNAL OF OCEANIC ENGINEERING
(2023)
Article
Geochemistry & Geophysics
Diego Silva Medeiros, Fernando Dario Almeida Garcia, Renato Machado, Jose Candido S. Santos Filho, Osamu Saotome
Summary: Sea clutter is a persistent issue in radar systems, and the K-distribution is a promising model to accurately represent sea signal variations. The cell-averaging CFAR (CA-CFAR) detector, due to its balance between performance and implementation, has been widely used to improve radar performance in clutter environments. This study assesses radar performance using a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture, and provides closed-form expressions for the probability of detection and the probability of false alarm.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Lenin Patricio Jimenez Jimenez, Fernando Dario Almeida Garcia, Maria Cecilia Luna Alvarado, Gustavo Fraidenraich, Eduardo Rodrigues de Lima
Summary: This study provides a general cell-averaging constant false alarm rate (CA-CFAR) performance analysis in homogeneous Weibull-distributed clutter environments. It derives generalized expressions for the probability of detection (PD) and probability of false alarm (PFA) that allow arbitrary values for the shape parameter. Monte-Carlo simulations confirm the derived expressions, showing improved radar detection with increasing shape parameter.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Information Systems
Zhihuo Xu, Hongchen Zhang, Yuexia Wang, Xiaoyue Wang, Shuaikang Xue, Weixue Liu
Summary: This paper proposes an effective approach for dynamic detection of offshore wind turbines by machine learning from spaceborne synthetic aperture radar (SAR) satellite data. The approach includes preprocessing radar images, selecting representative data for training, and utilizing mathematical morphology-based time series spatial data differentiation for monitoring wind turbine changes. The proposed approach demonstrates high accuracy and has the potential for global dynamic detection of offshore wind turbines.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Hicham Madjidi, Toufik Laroussi
Summary: In this study, an automatic bilateral censoring and detection method is proposed and analyzed for log-normal sea clutter using the AML-CFAR detector. By resorting to linear biparametric adaptive thresholds, a logarithmic amplifier is introduced to transform the distribution to Gaussian. The AML estimates of the unknown mean and standard deviation parameters are used to compute the censoring thresholds and estimate the detection threshold, resulting in better performance compared to state-of-the-art detectors in simulations on both simulated and real SAR images.
DIGITAL SIGNAL PROCESSING
(2023)
Article
Geochemistry & Geophysics
Huizhang Yang, Tao Zhang, Yaomin He, Yihua Dan, Junjun Yin, Benteng Ma, Jian Yang
Summary: This article introduces a target detection method for radar images called the constant false alarm rate (CFAR) detector. Traditional CFAR detectors run fast on small images but become time-consuming on large-sized images. The proposed GPU-oriented designs accelerate CFAR detectors, making them thousands of times faster than classical CFAR detectors, and enabling real-time target detection in large-size radar images.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Engineering, Electrical & Electronic
Mu Zhou, Qian Wang, Liangbo Xie, Yong Ma, Wei He
Summary: This study proposes a passive target positioning method based on entangled light quantum, which utilizes quantum entangled states to detect and localize targets, providing high accuracy and anti-interference positioning services. By converting the continuous light energy into the discrete photon number, calculating detection probabilities, and establishing a CFAR detection model, the proposed method achieves highly sensitive target detection and accurate positioning. This method demonstrates high detection probability and low positioning error.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Geochemistry & Geophysics
Zirui Chen, Alei Chen, Weijian Liu, Xiaoyan Ma
Summary: In this letter, a constant false alarm rate (CFAR) detector is proposed for skywave over-the-horizon radar (OTHR) under Weibull distribution. The objective function is formulated in the Bayesian framework, with regularization terms used to regularize the function. The proposed detector significantly improves the probability of detection, especially in the case of multitarget and clutter edge.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Tao Liu, Tao Tang, Weijian Liu, Gui Gao
Summary: This study focuses on the polarimetric whitening filter (PWF) used in constant false alarm rate (CFAR) ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. The research aims to obtain an accurate detection threshold by using different statistical models and derives the probability density function, probability of false alarm, and threshold through mathematical methods. Experimental results demonstrate that different statistical models with the same log-cumulants can achieve similar detection performance.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Geochemistry & Geophysics
Muhammad Amjad Iqbal, Andrei Anghel, Mihai Datcu
Summary: This letter proposes a novel method for coastline extraction using synthetic aperture radar data, which relies on the Doppler parameter to delineate coastlines in the absence of in-situ data and cloud-free optical images. Results indicate that utilizing scattering from dual and cross-polarization for coastline extraction is more reliable than using co-polarization.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Aerospace
Mohamed Sahed, Elhadi Kenane, Ali Khalfa, Farid Djahli
Summary: In this paper, two exact closed-form expressions for the probability of false alarm are derived for a homogeneous Gamma distributed clutter. These expressions are applicable to both the cell averaging CFAR (CA-CFAR) and the greatest of CFAR (GO-CFAR) detectors. The proposed expressions, given in terms of the Gauss hypergeometric function and the second Appell function, are numerically examined and validated by comparing them to counterparts computed using numerical integrals and Monte-Carlo simulations, considering various scenarios.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Sebastian A. Villar, Sebastian Torcida, Gerardo G. Acosta
JOURNAL OF MATHEMATICAL IMAGING AND VISION
(2017)
Article
Engineering, Civil
Sebastian A. Villar, Mariano De Paula, Franco J. Solari, Gerardo G. Acosta
IEEE JOURNAL OF OCEANIC ENGINEERING
(2018)
Article
Engineering, Electrical & Electronic
Sebastian A. Villar, Bruno V. Menna, Sebastian Torcida, Gerardo G. Acosta
IET RADAR SONAR AND NAVIGATION
(2019)
Article
Automation & Control Systems
Ignacio Carlucho, Mariano De Paula, Gerardo G. Acosta
Article
Biology
Jose A. Fernandez-Leon, Gerardo Acosta
Summary: By simulating the changes in non-variational free energy of a neural network during sleep transitions, the study investigated the role of slow wave frequencies and thalamic input in the self-organization process of the network. Results showed that the network exhibited characteristic differences under low and high thalamic drive, attributed to spike timing dependent plasticity.
Article
Operations Research & Management Science
Carolina Saavedra Sueldo, Ivo Perez Colo, Mariano De Paula, Sebastian A. Villar, Gerardo G. Acosta
Summary: In the era of Industry 4.0, smart manufacturing systems aim to achieve high flexibility and efficiency through automation, autonomous robotized systems, and efficient decision-making. This article proposes a software architecture based on ROS that integrates discrete-event simulators and enables fast integration with digital twins and autonomous decision-making systems. It demonstrates the practical application of the architecture through a complex case study of manufacturing plants.
ANNALS OF OPERATIONS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Jose A. Fernandez-Leon, Gerardo G. Acosta
Summary: This study aims to discuss the controversial issue of cognitive scalability for cognitive map emergence from the intersection of neuroscience and artificial intelligence. It proposes different neural structures that support the emergence of place maps and provides recommendations for advancing the field.
COGNITIVE COMPUTATION
(2022)
Article
Environmental Sciences
Florencia Jerez, Pamela B. Ramos, Veronica E. Cordoba, M. Federico Ponce, Gerardo G. Acosta, Marcela A. Bavio
Summary: Developing technological solutions using yerba mate waste can reduce environmental impact and accumulation in landfills. Yerba mate residues can be transformed into activated carbon, a versatile material with high surface area for energy storage. Chemically activated carbon was produced from the residues and characterized for its excellent electrochemical properties, making it suitable for use in supercapacitors.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2023)
Article
Multidisciplinary Sciences
Sebastian A. Villar, Adrian Madirolas, Ariel G. Cabreira, Alejandro Rozenfeld, Gerardo G. Acosta
Summary: Accurate identification and numerical estimation of aquatic organisms using echo detection techniques are challenging for marine researchers. This article presents a software architecture for echo data processing to obtain fish school descriptors for identification. The algorithms for automatic detection and classification, as well as the processing required for surface and bottom line detection, are thoroughly described.
Article
Computer Science, Information Systems
B. Menna, S. Villar, G. Acosta
IEEE LATIN AMERICA TRANSACTIONS
(2019)
Proceedings Paper
Computer Science, Information Systems
Esteban Giraldo, Gerardo Acosta, Carlos Verucchi, Miriam Ferrari
2017 XVII WORKSHOP ON INFORMATION PROCESSING AND CONTROL (RPIC)
(2017)
Proceedings Paper
Computer Science, Information Systems
Sebastian A. Villar, Franco J. Solari, Bruno V. Menna, Gerardo G. Acosta
2017 XVII WORKSHOP ON INFORMATION PROCESSING AND CONTROL (RPIC)
(2017)