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
Streit R.,Metron Inc.
Journal of Advances in Information Fusion | Year: 2013
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
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.
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 1.49M | Year: 2014
The objective of this project is to develop high-fidelity, computationally efficient, physics based models for electro-optical imaging systems that operate from an above-water platform and are used to detect underwater objects. The goal is to accurately simulate the output of such systems and provide meaningful quantitative measures of their performance based on environment and threat specifications, and to integrate this functionality within Navy Mine Warfare (MIW) Command & Control (C2) systems, such as the Netcentric Sensor Analysis for MIW (NSAM) system and MIW and Environmental Decision Aids Library (MEDAL).
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.