Entity

Time filter

Source Type

Reston, VA, United States

Streit R.,Metron Inc.
Journal of Advances in Information Fusion | Year: 2014

In multitarget tracking problems based on finite point process models of targets and measurements, it is known that the distribution of the Bayes posterior point process is a ratio of functional derivatives of a joint probability generating functional. It is shown here that these functional derivatives can be found by evaluating ordinary derivatives. The method is exact, not approximate. Several examples are presented, including multisensor target tracking and extended-target tracking. The method is well suited to the needs of particle filter implementations. © 2014 JAIF. Source


Probability generating functionals (PGFLs) for finite point processes are used to derive the probability hypothesis density (PHD) filter and intensity filter (iFilter) for multitarget tracking. Presenting them in a common PGFL framework makes manifest their similarities and differences. A significant difference is their measurement model-the PHD filter uses an exogenous clutter model and the iFilter uses an endogenous scattering model. © 2013 JAIF. Source


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.99K | Year: 2015

As the air transportation system becomes increasingly autonomous over the next twenty years, there will be an increasing need for monitoring capabilities that operate in the background to identify anomalous behaviors consistent with either safety or efficiency deficiencies. Today, these behaviors are largely detected after an incident has occurred. In July 2013, an Asiana Boeing 777 flew too low approaching San Francisco International Airport (SFO), its tail hitting a seawall and crashing into the runway. Three people died and 180 were injured. Since the weather was clear and visibility unimpeded, part of the instrument landing system (the glideslope transmitter) was offline for service, thus requiring pilots to land visually. The National Transportation Safety Board (NTSB) found that the Asiana pilots' reliance on the automated flight systems was a key factor in that crash. Further analysis by the Wall Street Journal revealed that foreign pilots required more "go-arounds" at SFO than U.S. pilots in the six weeks prior to the Asiana Airlines crash (i.e., when the glideslope transmitter was down), indicating a greater difficulty in executing the landing via visual approach. This type of anomalous behavior could have been detected prior to the crash. All of the data was available, but no one was looking at it to see these consistent, yet anomalous behaviors. Metron proposes to develop a semi-autonomous background monitoring system to apply this type of data mining and data discovery to recent historical track repositories in order to identify opportunities for improvements to safety and efficiency in airspace operations. Metron proposes a statistical approach that uses historical flight data to develop models of normal behavior, and then apply statistical methods to identify outliers under one or more indicators. Metron has used similar approaches for anomaly detection systems developed and delivered to operational customers in the land and maritime domains.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 749.93K | Year: 2015

ABSTRACT: The concept of fully adaptive radar (FAR) seeks to exploit all available degrees of freedom on transmit and receive in order to maximize radar system performance. Of key importance is the concept of closed loop radar operation via feedback from the receiver to transmitter for guiding the next illumination. The first goal of this project is to develop a cognitive radar system definition that identifies key components of cognitive radar, provides formal definitions for those components, and relates them to concepts in cognition. The second goal is to develop a theoretical framework for a FAR system that includes specification of the feedback mechanism from the receiver to the transmitter and specification of performance metrics to assess FAR system performance. The third goal is to develop application-specific models, simulations, and analysis methods to demonstrate and measure the performance improvement achieved by FAR systems over standard feed-forward radar systems. The fourth goal is to demonstrate the real-time operation of a FAR system on a cognitive radar testbed in a laboratory setting. BENEFIT: Radar systems are crucial for robust surveillance, target acquisition, and reconnaissance in all weather conditions and over wide ranges of interest. Most radar systems employ a feed-forward processing chain in which they first perform some low-level processing of received echo data and then pass the processed data on to some higher-level processor, which extracts information to achieve a system objective. The concept of fully adaptive radar seeks to exploit all available degrees of freedom on transmit and receive in order to maximize radar system performance. The application of artificial cognition to radar systems thus offers much promise for improved sensing as well as the creation of new sensing modalities. Research into cognitive systems is currently in its infancy and the results of this project will help define the field. The modeling, simulation, analysis, and experimentation tools developed under this project are quite general. As such, they can be applied to a variety of radar systems for a variety of missions, and can be translated into other domains such as computing, autonomous vehicles, and perhaps even neuroscience and evolutionary biology. The project will develop the fundamental tools necessary to design and analyze a cognitive processing system, and will enable further research in the abovementioned diverse fields.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 749.94K | Year: 2016

There is a documented high priority Navy need to substantially reduce Mine Countermeasures (MCM) tactical survey mine hunting and neutralization timelines in support of relevant Operational Plans (OPLANs) and Concept of Operation Plans (CONPLANs). Hence there is also a need to minimize the time and resources required to conduct MCM baseline surveys (and re-surveys) in key Continental United States (CONUS) and Outside Continent United States (OCONUS) Maritime Homeland Security (MHS) and Navy areas of interest (AOIs). Hence the purpose of the innovative autonomous underwater vehicle (AUV) based geotechnical survey operations related sensing and processing initiatives proposed here is to investigate advanced, but low cost, AUV based, on-the-fly doctrinal bottom type (DBT) direct measurement approaches in Navy/MHS areas of interest.

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