1,149 research outputs found
APPLYING MACHINE LEARNING FOR COP/CTP DATA FILTERING
Student Thesis (NPS NRP Project Related)Accurate tracks and targeting are key to providing decision-makers with the confidence to execute their missions. Increasingly, multiple intelligence, surveillance, and reconnaissance (ISR) assets across different intelligence sources are being used to increase the accuracy of track location, resulting in the need to develop methods to exploit heterogeneous sensor data streams for better target state estimation. One of the algorithms commonly used for target state estimation is the Kalman Filter (KF) algorithm. This algorithm performs well if its covariance matrices are accurate approximations of the uncertainty in sensor measurements. Our research complements the artificial intelligence/machine learning (AI/ML) efforts the U.S. Navy is conducting by quantitatively assessing the potential of using an ML model to predict sensor measurement noise for KF state estimation. We used a computer simulation to generate sensor tracks of a single target and trained a neural network to predict sensor error. The hybrid model (ML-KF) was able to outperform our baseline KF model that uses normalized sensor errors by approximately 20% in target position estimation. Further research in enhancing the ML model with external environment variables as inputs could potentially create an adaptive state estimation system that is capable of operating in varied environment settings.NPS Naval Research ProgramThis project was funded in part by the NPS Naval Research Program.Outstanding ThesisCaptain, Singapore ArmyApproved for public release. Distribution is unlimited
Overview of Key Technologies for Water-based Automatic Security Marking Platform
Water-based automatic security marking platform composed of multifunctional underwater robots and unmanned surface vessel has become the development trend and focus for exploring complex and dangerous waters,and its related technologies have flourished and gradually developed from single control to multi-platform collaborative direction in complex and dangerous waters to reduce casualties. This paper composes and analyzes the key technologies of the water-based automatic security marking platform based on the cable underwater robot and the unmanned surface vessel, describes the research and application status of the key technologies of the water-based automatic security marking platform from the aspects of the unmanned surface vessel, underwater robot and underwater multisensor information fusion, and outlooks the research direction and focus of the water automatic security inspection and marking platform
Proceedings of the Fourth MIT/ONR Workshop on Distributed Information and Decision Systems Motivated by Command-Control-Communications (C3) Problems, June 15-June 26, 1981, San Diego, California
"OSP number 85552"--Cover.Library has v. 2 only.Includes bibliographies.Workshop suppported by the Office of Naval Research under contract ONR/N00014-77-C-0532edited by Michael Athans ... [et al.].v.1. Surveillance and target tracking--v.2. Systems architecture and evaluation--v.3. Communication, data bases & decision support--v.4. C3 theory
Autonomous sea craft for search and rescue operations : marine vehicle modelling and analysis.
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2011.Marine search and rescue activities have been plagued with the problem of risking the lives of rescuers in
rescue operations. With increasing developments in sensor technologies, it became a necessity in the
marine search and rescue community to develop an autonomous marine craft to assist in rescue
operations. Autonomy of marine craft requires a robust localization technique and process. To apply
robust localization to marine craft, GPS technology was used to determine the position of the marine craft
at any given point in time. Given that the operational environment of the marine was at open air, river, sea
etc. GPS signal was always available to the marine craft as there are no obstructions to GPS signal.
Adequate cognizance of the current position and states of an unmanned marine craft was a critical
requirement for navigation of an unmanned surface vehicle (USV). The unmanned surface vehicle uses
GPS in conjunction with state estimated solution provided by inertial sensors. In the absence of the GPS
signal, navigation is resumed with a digital compass and inertial sensors to such a time when the GPS
signal becomes accessible.
GPS based navigation can be used for an unmanned marine craft with the mathematical modelling of the
craft meeting the functional requirements of an unmanned marine craft. A low cost GPS unit was used in
conjunction with a low cost inertial measurement unit (IMU) with sonar for obstacle detection. The use of
sonar in navigation algorithm of marine craft was aimed at surveillance of the operational environment of
the marine craft to detect obstacles on its path of motion. Inertial sensors were used to determine the
attitude of the marine craft in motion
AN INFORMATION THEORETIC APPROACH TO INTERACTING MULTIPLE MODEL ESTIMATION FOR AUTONOMOUS UNDERWATER VEHICLES
Accurate and robust autonomous underwater navigation (AUV) requires the fundamental task of position estimation in a variety of conditions. Additionally, the U.S. Navy would prefer to have systems that are not dependent on external beacon systems such as global positioning system (GPS), since they are subject to jamming and spoofing and can reduce operational effectiveness. Current methodologies such as Terrain-Aided Navigation (TAN) use exteroceptive imaging sensors for building a local reference position estimate and will not be useful when those sensors are out of range. What is needed are multiple navigation filters where each can be more effective depending on the mission conditions. This thesis investigates how to combine multiple navigation filters to provide a more robust AUV position estimate. The solution presented is to blend two different filtering methodologies utilizing an interacting multiple model (IMM) estimation approach based on an information theoretic framework. The first filter is a model-based Extended Kalman Filter (EKF) that is effective under dead reckoning (DR) conditions. The second is a Particle Filter approach for Active Terrain Aided Navigation (ATAN) that is appropriate when in sensor range. Using data collected at Lake Crescent, Washington, each of the navigation filters are developed with results and then we demonstrate how an IMM information theoretic approach can be used to blend approaches to improve position and orientation estimation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited
INTEROPERABILITY FOR MODELING AND SIMULATION IN MARITIME EXTENDED FRAMEWORK
This thesis reports on the most relevant researches performed during the years of the Ph.D. at the Genova University and within the Simulation Team. The researches have been performed according to M&S well known recognized standards. The studies performed on interoperable simulation cover all the environments of the Extended Maritime Framework, namely Sea Surface, Underwater, Air, Coast & Land, Space and Cyber Space. The applications cover both the civil and defence domain. The aim is to demonstrate the potential of M&S applications for the Extended Maritime Framework, applied to innovative unmanned vehicles as well as to traditional assets, human personnel included. A variety of techniques and methodology have been fruitfully applied in the researches, ranging from interoperable simulation, discrete event simulation, stochastic simulation, artificial intelligence, decision support system and even human behaviour modelling
Space-based Global Maritime Surveillance. Part I: Satellite Technologies
Maritime surveillance (MS) is crucial for search and rescue operations,
fishery monitoring, pollution control, law enforcement, migration monitoring,
and national security policies. Since the early days of seafaring, MS has been
a critical task for providing security in human coexistence. Several
generations of sensors providing detailed maritime information have become
available for large offshore areas in real time: maritime radar sensors in the
1950s and the automatic identification system (AIS) in the 1990s among them.
However, ground-based maritime radars and AIS data do not always provide a
comprehensive and seamless coverage of the entire maritime space. Therefore,
the exploitation of space-based sensor technologies installed on satellites
orbiting around the Earth, such as satellite AIS data, synthetic aperture
radar, optical sensors, and global navigation satellite systems reflectometry,
becomes crucial for MS and to complement the existing terrestrial technologies.
In the first part of this work, we provide an overview of the main available
space-based sensors technologies and present the advantages and limitations of
each technology in the scope of MS. The second part, related to artificial
intelligence, signal processing and data fusion techniques, is provided in a
companion paper, titled: "Space-based Global Maritime Surveillance. Part II:
Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic
Systems Magazin
Air Force Institute of Technology Research Report 2012
This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics
Air Force Institute of Technology Research Report 2004
This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics
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