Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 149.95K | Year: 2015
ABSTRACT: This project addresses the development and evaluation of high-quality low-dimensional (HQLD) sensor data selection and fusion (SDSF) algorithms. The objective is to develop algorithms that process the data from multiple sensors to reduce dimensionality while retaining the information content of the full-dimension data. An HQLD-SDSF algorithm will be developed within the context of a likelihood ratio detection and tracking (LRDT) system. LRDT is a nonlinear Bayesian filtering technique that incorporates target detection, localization, and tracking, and naturally fuses multi-sensor data via sensor likelihood functions. The exponentially embedded families (EEF) approach to probability density function (PDF) modeling and feature selection will be used to select and fuse lower-dimensional sensor data in an optimal and tractable manner. The LRDT-HQLD-SDSF algorithm will take system objectives and knowledge of the current target state from the LRDT tracking system and an information-theoretic measure of current data quality from the EEF constructed likelihood function, and balance these with system constraints to determine the best set of low-dimension sensor data to send to a central processor to achieve system objectives under the current situation. Performance will be demonstrated on a simulated multi-sensor system consisting of a passive radar sensor, an infrared sensor, and a visible light sensor.; BENEFIT: Many different types of sensor systems have been developed for both defense and commercial applications, including radar, sonar, electro-optical, infrared, visible light, hyperspectral, and electromagnetic, to name a few. Each measures a different characteristic of the target or scene and multiple sensors together can provide a more complete picture of the situation than any one sensor alone. Multi-sensor data fusion algorithms seek to integrate the data from disparate sensors so that information relevant to a particular system objective is combined and distilled to a useful form. There is a need to select data from the most relevant sensors and to reduce the dimension of the available data while retaining mission-critical information in order to simplify processing, eliminate unnecessary and possibly distracting information, and/or satisfy system constraints. The goal of this project is to develop algorithms that reduce the dimension of the data at multiple sensors in an optimal way taking into account system objectives, system constraints, and the observed quality of the data. The benefits are improved detection, localization, and tracking systems, which have applicability in intelligence, surveillance, and reconnaissance (ISR) systems within the Department of Defense, Department of Homeland Security, and intelligence and law enforcement agencies as well as numerous commercial applications, including medical, pharmaceutical, automotive, manufacturing, and robotics.
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase II | Award Amount: 749.83K | Year: 2014
ABSTRACT: The goal of this project is to develop a site-specific physics-based model and simulation capability for MIMO radar clutter and to perform an extensive and exhaustive validation of the model through statistical and experimental data analysis. The foundation of our model is a physics-based bistatic scattering model (PBSM) capable of predicting clutter patch statistical properties including the probability distribution of clutter patch returns and their correlation properties as a function of frequency, time, space, polarization, and angle, given the radar sensor properties, site-specific observing geometry, and site-specific scattering environment characteristics. The PBSM will be incorporated into a MIMO radar system model and computer simulation tool capable of generating MIMO radar clutter samples. The MIMO radar system model will include multiple airborne platforms, a variety of waveforms, site-specific modeling of clutter patch geometry, and a variety of natural and urban clutter classes. The simulator will include a companion set of tools for statistical and experimental model validation and inference. Finally, the models will be extensively validated experimentally collected data. BENEFIT: The availability of realistic clutter models and simulated clutter data is critical for advanced system design and performance analysis for MIMO GMTI radar systems. This tool will provide such a capability, which currently does not exist.
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: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.88K | Year: 2015
ABSTRACT:Metron, Incorporated proposes to leverage the strike planning optimization methodology it previously developed for the USAF, its intimate knowledge of STORM, and its unrivaled expertise in developing innovative Department of Defense (DoD) M&S solutions to implement an optimal and efficient multi-criteria utility value function for assigning air packages to targets within STORM. This capability will empower STORM analysts to perform integrated cost-effectiveness modeling. Metron possesses existing, proven algorithms developed under the USAFs Fast Master Air Attack Plan (MAAP) initiative to optimize the allocation of strike resources against targets by simultaneously minimizing expected cost, maximizing expected effectiveness, and satisfying user-imposed constraints. We also have extensive experience employing STORM and the deep understanding of its architecture required to modify the model efficiently and securely. Our innovative approach involves externalizing the strike planning process in STORM to simplify development, maximize software reuse, and facilitate commercialization.BENEFIT:Current U.S. Air Force (USAF) and Joint modeling and simulation (M&S) analyses follow an inefficient process wherein cost and effectiveness are modeled independently and require imperfect, manual integration of results. Incorporating a concept of cost along with algorithms designed to minimize it into warfare models would provide a significantly improved capability to assess the fiscal feasibility and sustainability of plans, CONOPS, and acquisition strategies in diverse and rapidly-evolving scenarios. This capability would improve the effectiveness and impact of USAF and Joint studies and reduce the expense associated with conducting and integrating separate cost and warfare analyses.
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 I | Award Amount: 79.97K | Year: 2015
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 748.75K | Year: 2016
As the air transportation system becomes more autonomous in the coming years, there will be an increasing need for monitoring capabilities that operate in the background to identify anomalous behaviors indicating 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. This type of anomalous behavior (i.e. foreign pilots consistently flying too low into SFO on visual approach) could have been detected prior to the crash because the data was available, but no one was looking at it. Metron proposes to develop a semi-autonomous background monitoring system to apply this type of data mining and data discovery to flight track data in order to identify opportunities for improvements to safety and efficiency in airspace operations. In the Phase I effort, Metron demonstrated a proof-of-concept statistical approach that we call the Normalcy Score Broker (NSB), which uses historical flight data to develop models of normal behavior, and then applies statistical methods to combine multiple features into a single score for identifying outliers. Metron has used this same NSB technique to develop operational systems for customers in the land and maritime domains. In the Phase II, we propose to extend the techniques to process at scale, whether for real-time streaming data or for efficient analyses on forensic repositories. In addition to generating new features associated with clusters of flights interacting with each other, we propose to incorporate greater context (e.g., flight behavior in the presence of convective weather) and learning techniques to reduce false positives based on operator feedback on the relevance of the reported anomalies. We will test and evaluate our software on the NASA Cloud-based SMART-NAS Test Bed.
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.
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.
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 749.89K | Year: 2015
ABSTRACT:This project addresses the development and benchmarking of advanced algorithms to track modern supermaneuverable targets. The objective is to develop, demonstrate, and validate an assessment tool that provides future radar architecture design performance requirements to the acquisition community for tracking algorithms that will succeed against these modern threats. In Phase I, we enumerated and tested a wide set of tracking approaches, from traditional, fielded Kalman filter algorithms including the extended Kalman filter and unscented Kalman filter, to state-of-the-art nonlinear filtering algorithms, such as the resampling particle filter and homotopy particle filter. We benchmarked these algorithms in simulation against a set of supermaneuvering targets using simulated radar measurements. The effort showed the failure mechanisms of standard Kalman-based approaches and brought to light the significant improvements available from modern, cutting edge nonlinear filtering algorithms. In Phase II, we propose to extend and refine the trackers developed in Phase I, test the methods with high-fidelity flight data, and use these results to produce recommendations for future systems.BENEFIT:The anticipated results from the project are: (i) an analysis of modern nonlinear filtering algorithms on modern supermaneuverable threats which shows military-relevant performance improvements over conventional tracking algorithms, to include: increased detection range and improved target localization, (ii) Matlab-level prototyping of these new tracking algorithms that are robust and computationally efficient, and (iii) a method of using these tools to predict in-theatre performance and recommend radar design and settings to optimize the performance. The immediate benefit of the Phase II tracking capability is to provide the Air Force with a quantitative assessment of the performance improvement possible when using modern tracking algorithms. Furthermore, the analysis will provide methods for determining how to design future systems to maximize in-theatre performance by selecting radar parameters and surveillance platform trajectories.