Ai Radar Tracker For Sar Radar Detection Field Trial
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Author | : R.B. Fitzgerald |
Publisher | : |
Total Pages | : 30 |
Release | : 1998 |
Genre | : |
ISBN | : |
The goal of this field project was the collection of high-quality, digital radar video to support the development of radar processing technologies for small target detection, including the Raytheon "AI Tracker". Oceans Ltd. developed a range of calibrated radar targets constructed of commercially available fishing floats and a radar-reflective mesh. The smallest targets had a radar cross section (RCS) approximating that of a worst-case search and rescue (SAR) target: a half-submerged human head. A high-speed scanner (120 rpm) developed by MIL Systems Engineering was installed on the support vessel, the CCGS J.E. Bernier and interfaced with the Sigma MRI and the Raytheon AI Tracker. Software to control the high-speed scanner was developed by Sigma Engineering under a separate contract. Equipment installations on board the support vessel were completed with the assistance of Canadian Coast Guard (CCG) Engineering and Technical Services personnel at the CCG base in St. John's.
Author | : Fitzgerald, Reg |
Publisher | : [Montréal] : The Centre |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Author | : Reg Fitzgerald |
Publisher | : |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Author | : Raytheon Canada Limited |
Publisher | : [Montréal] : Transportation Development Centre, Transport Canada |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Author | : P. Scarlett |
Publisher | : |
Total Pages | : 68 |
Release | : 1998 |
Genre | : |
ISBN | : |
This report describes the development and preliminary testing of the prototype Search and Rescue Artificial Intelligence Tracker (SARAIT). The SARAIT processes plots from any conventional marine radar using M of N (M target detections in N scans) integration and multiple hypothesis tracking (MHT) to detect small, awash, slowly drifting targets such as liferafts, person in water (PIWs) and wreckage. The SARAIT is implemented on two dual-Pentium Pro single-board computers. It was operated in real time during offshore data-gathering trials and was tested with a small subset of the taped data recorded while sailing at 8 to 10 kn in 3.3 to 3.8 m seas. The SARAIT reliably detected very small PIW-sized targets at 1 to 2 nmi (depending on the clutter intensity) and small liferaft-sized targets at 2 to 3.5 nmi, all with less than 5 false detections per hour. Longer detection ranges are expected to result from the more involved testing planned for early 1999.
Author | : Scarlett, Peter |
Publisher | : [Montréal] : Transportation Development Centre, Safety and Security |
Total Pages | : |
Release | : 1997 |
Genre | : Artificial intelligence |
ISBN | : |
Author | : Transportation Development Centre (Canada) |
Publisher | : |
Total Pages | : 14 |
Release | : 1999 |
Genre | : Publishers' catalogs |
ISBN | : |
Author | : Maciej Rysz |
Publisher | : Springer Nature |
Total Pages | : 282 |
Release | : 2023-01-18 |
Genre | : Mathematics |
ISBN | : 3031212258 |
This carefully curated volume presents an in-depth, state-of-the-art discussion on many applications of Synthetic Aperture Radar (SAR). Integrating interdisciplinary sciences, the book features novel ideas, quantitative methods, and research results, promising to advance computational practices and technologies within the academic and industrial communities. SAR applications employ diverse and often complex computational methods rooted in machine learning, estimation, statistical learning, inversion models, and empirical models. Current and emerging applications of SAR data for earth observation, object detection and recognition, change detection, navigation, and interference mitigation are highlighted. Cutting edge methods, with particular emphasis on machine learning, are included. Contemporary deep learning models in object detection and recognition in SAR imagery with corresponding feature extraction and training schemes are considered. State-of-the-art neural network architectures in SAR-aided navigation are compared and discussed further. Advanced empirical and machine learning models in retrieving land and ocean information — wind, wave, soil conditions, among others, are also included.
Author | : Uttam K. Majumder |
Publisher | : Artech House |
Total Pages | : 290 |
Release | : 2020-07-31 |
Genre | : Technology & Engineering |
ISBN | : 1630816396 |
This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.
Author | : |
Publisher | : |
Total Pages | : 12 |
Release | : 1995 |
Genre | : Government publications |
ISBN | : |