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A02-132 TITLE: Network Assisted Global Positioning System (GPS) Direct Y Acquisition TECHNOLOGY AREAS: Electronics ACQUISITION PROGRAM: PM, Global Positioning System (GPS) OBJECTIVE: The Army’s concept for the Objective Force includes Situational Dominance which enables our forces to engage the enemy in “places of our choosing”. Under this topic, a technique will be developed that will greatly bolster GPS reception in low signal and degraded signal environments (e.g., tunnels, buildings, under tree canopy and within proximity to RF transmissions). DESCRIPTION: In meeting the requirements of the Federal Communications Commission's (FCC) E911 mandate, commercial technologies have been developed and proven that significantly improve a GPS receiver’s capability to compute a users position in low signal environments. By providing the receiver with necessary information over a network link (e.g. time, satellite ephemeris, differential corrections and other information), a receiver is enabled to acquire the satellite’s signals in degraded environments with the improved knowledge about the signal’s uncertainties. The current technology developed to meet the FCC E911 mandate operates on the commercial civilian GPS signal known as Course Acquisition or C/A code. Commercial technologies developed for this application require significant adaptation because military GPS receivers are required to operate on a cryptographic signal known as Precise Code or P/Y, as opposed to C/A code. This is not a trivial translation as the P/Y code has a much longer repetition interval and a much faster chip rate with comparison to the commercial C/A code. With this capability, many of the elements of the Future Combat System and Objective Force, including warriors, ground manned/unmanned platforms and low speed/low altitude air vehicles will be provided with a robust positioning capability in the execution of their missions, at all times and in all places. PHASE I: The purpose of Phase I is to conduct research and risk reduction alternative trade studies necessary to define the concepts for operation and architecture definition of a Network Assisted GPS implementation operating with P/Y code. The contractor shall prepare a defined system specification implementing the architecture and demonstrate the key technology areas through laboratory experimentation and modeling and simulation. The contractor shall deliver a final report containing a detailed plan for the building and demonstration of the prototype hardware required in Phase II. The final report shall also describe the conceived final end-state PHASE II: The Phase II objective is to design, build and demonstrate a prototype system incorporating the technology architecture defined in Phase I, implementing Network Assisted GPS operating on P/Y code. PHASE III: Potential markets may include sales to government agencies tasked with homeland security, search and rescue missions and other missions where immediate accurate positioning is essential. REFERENCES: 1) SS-GPS-300, "System Specification for the Navstar Global Positioning System". 2) ICD-GPS-200, "NAVSTAR GPS Space Segment/Navigation User Interface (U)". KEYWORDS: Antijam, Applique, Handheld Receivers, Precision Lightweight GPS Receiver (PLGR), Defense Advanced GPS Receiver (DAGR). A02-133 TITLE: Automated Fusion of Digital Elevation Models TECHNOLOGY AREAS: Sensors ACQUISITION PROGRAM: PM, Combat Terrain Information Systems (CTIS) OBJECTIVE: To develop automated techniques that merge and harmonize elevation data from diverse sources into a single coherent digital elevation model (DEM). DESCRIPTION: There are a large number of products that can be derived from DEMs. Both military and civil communities exploit derived products, such as slope, aspect, and irradiance, for a variety of applications. Some applications include land capability classification, resistance to uphill transport, correction of remotely sensed images, evapotranspiration, and vegetation and soil studies (Burrough and McDonnell, 1998). The technology to create elevation data has grown rapidly. Light Detection and Ranging (LIDAR), Interferometric Synthetic Aperture Radar (IFSAR), conventional stereo image correlation, and other emerging capabilities provide different approaches to elevation data production (ASPRS, 2001; Toth et al., 1999). Each technology has unique strengths and weaknesses and offers the potential to produce datasets of varying quality and resolution. The Government seeks a flexible capability to intelligently merge multiple elevation datasets over a broad area into a single coherent matrix. The solution must address problems of translation, scaling, and rotational differences, as well as linear and non-linear data trends and random errors in the data. Issues of coordinate systems, datums, resolution, and data quality must be addressed. The Government seeks a contractor to investigate methods and develop software to perform the intelligent merging of multiple digital elevation models with differing resolutions and variable accuracy. A simple resampling of matrix data or formation of a Triangulated Irregular Network (TIN) will not be sufficient. PHASE I: The contractor will accomplish the following research goals: 1) develop DEM data fusion techniques, 2) produce a “best of” (“best map”) composite DEM over a required region, 3) improve DEM accuracy, and 4) improve DEM confidence. The contractor will provide a report documenting the elevation data merging algorithms and demonstrate/deliver software to perform the data fusion tasks. The Government will provide sample data sets and definitions of typical fusion tasks. PHASE II: The contractor will develop/deliver a Component Object Model (COM)-based integrated elevation data merging software application that interoperates with commercial geographic information systems. The contractor will be required to import/export the following formats: 2D Array, NIMA DTED, NITF for DTED level 3-4-5, USGS DEM, ESRI GRID, ESRI GRIDASCII, and ERDAS IMAGINE. The contractor will demonstrate the integrated elevation data fusion capabilities with the required formats, in addition to demonstrating system interoperability. PHASE III: This SBIR would result in a technology with broad applications in the military and civil communities by providing datasets of improved quality and resolution. For military applications, the techniques proposed in this SBIR will provide terrain information systems, such as the Combat Terrain Information Systems (CTIS) Digital Topographic Support System (DTSS), with DEMs of improved accuracy and confidence, and therefore enable these systems to produce higher quality products to help the battlefield commander make better real-time and near real-time decisions. For the commercial sector, the new technology would provide improved DEMs for input in a wide variety of applications ranging from areas such as surface water hydrology to modeling soil erosion hazards to determining optimal extraction of timber from natural forests (Burrough and McDonnell, 1998). REFERENCES: 1) American Society of Photogrammetry and Remote Sensing (ASPRS), 2001. Digital Elevation Model Technologies and Applications: The DEM Users Manual (David Maune, editor). 2) Toth, C. K., and D. A. Grejner-Brzezinska, 1999. DEM Extraction From High-Resolution Digital Imagery and Laser Altimetry Sensor Integration for Improved Surface Modelling, Geomatics Info Magazine, 13, no. 6, pp. 42-45. 3) Burrough, P. A., and McDonnell, R. A. (1998). Principles of Geographical Information Systems. Oxford University Press, Oxford, 333 pp. KEYWORDS: DEM, Fusion, Best Map A02-134 TITLE: Developing a Seamless Integration Between Machine Learning Techniques and Rule-Based Classification of Remotely Sensed Imagery TECHNOLOGY AREAS: Battlespace ACQUISITION PROGRAM: PM, Combat Terrain Information Systems (CTIS) OBJECTIVE: To develop a seamless integration between rules derived from machine-learning techniques and a rule-based image classifier that is transparent to the remote sensing analyst. DESCRIPTION: Remotely sensed imagery has long been a valuable source of information to the terrain analyst in both civil and military applications. Military applications include development of tactical decision aids. Civil applications include site suitability studies, urban planning, and resource management. For this imagery to be useful, it often needs to be classified into recognizable land covers or land uses. Traditionally, terrain analysts perform supervised or unsupervised classifications using only the imagery’s spectral information. However, land cover/use classification accuracy of remotely sensed data can be increased by incorporating ancillary raster data layers. Digital Elevation Models (DEMs) are a common source of ancillary data – elevation, slope, aspect, etc. Ancillary data also includes GIS thematic layers such as soils, geology, etc. However, it is statistically invalid to merge categorical data (soils) with spectral bands when using a traditional statistical classifier such as maximum likelihood classifier. Recently, rule-based classification systems using rules based on machine-learning have been shown to be powerful means of incorporating ancillary data. The problem is this – rules based on machine-learning techniques, such as CART (class and regression tree) analysis, are currently generated in commercial statistical analysis packages. These packages are generally not designed to accomodate raster-based data as input. Thus, the sample raster training data has to be converted into formats supported by the statistical analysis package. After rules are generated, analysts then have to reformat them extensively before they can be used in a rule-based image classifier. Currently no mechanism exists to import rules directly into any of the major COTS image processing packages. Since rules generally are not portable from one image scene to the next, for rules to be useful it must be easy to generate them and use them in a classifier. The current process is too time consuming, disjointed, and requires education in too many disparate fields – statistics, computer programming, and remote sensing analysis. This SBIR’s objective is to design and build a seamless cohesive image processing system that combines the machine-learning/rule-generation functions of a statistical analysis package with the image classification functions of an image processing package. To the analyst it should appear that he is using an image processing system that has a machine learning/rule generation capability option. PHASE I: The contractor needs to accomplish two research goals using Government provided LANDSAT and ancillary test data. First, develop a methodology and a preliminary system design that would seamlessly integrate the rules generated through machine-learning with a rule-based classifer. The contractor will have to state specifically in this design how this integration will occur and with what (if any) COTS software. Although ERDAS Imagine is the Army’s predominant image processing software, the Government will consider others. The design should also include any development ideas that would facilitate rule-generation, rule editing (pruning), rule-tracing and examining the impact of specific rules on the subsequent image classification. Thus, the functions an analyst needs to create, examine, and run the rules to classify an image are to be part of the contractor’s design. Second, using test data provided by the Government, the contractor must demonstrate a capability to use machine-learning techniques to create rules. These rules do not have to be used in a rule-based classifier (that will occur in Phase II) and the Government will examine the resulting rules for validity. PHASE II: The contractor will complete the system design and develop the processing capabilities that are defined in Phase I into a prototype system. The prototype system will further develop and enhance the capabilities developed in Phase I. The prototype will show the seamless linkage between the rules generated through machine learning and the subsequent image classification based on those rules. Testing will occur with as much data as time and budgetary constraints allow. Ideally, a test case will be developed over an Army-determined area of interest. PHASE III DUAL USE APPLICATIONS: This SBIR would result in a technology with broad applications in the military and civil communities. Land use/land cover mapping is one of the most common uses of remotely sensed imagery for both communities. For the military, land use/land cover classification is used as inputs to derive tactical decision aids such as identifying helicopter landing zones, identifying areas of cover and concealment, transportation corridors, etc. For the civil community, land use/land cover is used for site suitability studies, urban planning, resource management, etc. Land use and land cover themes are often used as thematic layers in geographic information systems (GIS) applications. As ancillary data becomes more readily available, both military and civilian users will be looking for techniques that make use of that data to improve image classification. These techniques have to be easy to use and intuitive to the image analyst. This is not the case now. Currently, it is a time-consuming and cumbersome process to use or convert the rules that are generated through machine-learning into a rules-based image classifier. REFERENCES: 1) Huang, X, and J. R. Jensen, 1997. A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data, Photogrammetric Engineering and Remote Sensing, 63(10): 1185-1194. 2) Hutchinson, C. F., 1982. Techniques for combining LANDSAT and ancillary data for digital classification improvement. Photogrammetric Engineering & Remote Sensing, 48: 123-130. 3) Jensen, J. R., 1996. Introductory Digital Image Processing: A Remote Sensing Perspective, Second Edition, Prentice Hall, Upper Saddle River, New Jersey, 316 p. 4) Lawrence, Rick, Andrea Wright, 2001. Rule-based classification systems using classification and regression tree (CART) analysis, Photogrammetric Engineering and Remote Sensing, 67(10): 1137-1142. KEYWORDS: Rule-Based Classification, Image Classification, Machine Learning, Classification and Regression Tree (CART) Analysis A02-135 TITLE: Advancing Hyperspectral Signature Integration with Airborne and Ground-based Laser Technology TECHNOLOGY AREAS: Battlespace ACQUISITION PROGRAM: PM, Combat Terrain Information Systems (CTIS) OBJECTIVE: To develop improved methodologies and techniques to identify, classify, and integrate hyperspectral signatures with high-resolution elevation data generated from airborne and ground based laser-scanning technologies. DESCRIPTION: The airborne and ground based laser systems have significantly matured to enable the acquisition of high-resolution digital elevation data in an operational manner. The airborne and ground based laser sensors are now collecting ground and above ground surface points at sub-meter spacing which can be processed into highly complex geo-referenced mass points with a vertical accuracy of 15 cm or less. Many cultural and vegetation features are included at these resolutions and accuracies. When multiple return laser-derived elevation data is viewed as mass points, vegetation features are represented as data clouds. This high density of laser mass points can be use to determine the height of vegetation features and building, but does not contain any information to discern what type of species or building materials are represented in the data. It is also difficult to discriminate low-lying grasses from the open ground surfaces within the laser mass point data. Hyperspectral imaging creates a large number of images from contiguous, rather that disjoint, regions of the spectrum, with increasing levels of resolution. This increased sampling of the spectrum provides a great increase in information. Many application tasks which were previously impractical or impossible can be accomplished with hyperspectral data sources. A primary goal of using hyperspectral data is to discriminate, classify, identify as well as quantify materials. When analyzing hyperspectral data, difficulty occurs when a scene pixel is mixed linearly or nonlinearly by different materials resident in the pixel. The integration and utilization of laser mass points combined with hyperspectral data has the potential to resolve these present conflicts with mixed pixels. These recent advances in hyperspectral imaging can be used for augmenting the capability of the airborne and ground based laser derived elevation data. In particular, the laser based elevation surfaces provide useful slope information and when combined with hyperspectral signatures may be used to decompose the topographic response into vegetation and ground surface contributions. Both of these data sources provide valuable information, but are typically processed with the available analysis techniques that are available for each individual data source and then are later integrated subjectively. Methodology that is capable of jointly and objectively extracting and using information from hyperspectral and laser elevation data types to model terrestrial features over extensive areas is critically needed, but not yet developed. The development of a holistic research approach that utilizes the combined capabilities of hyperspectral signatures with geo-referenced laser mass points is the primary focus and objective of this topic. The integration of these technologies will have the potential for significant enhancements in the responsiveness, flexibility, and performance of numerous military systems and operations PHASE I: The contractor shall evaluate the various hyperspectral component technologies that need to be combined to accurately identify, delineate, and discriminate both cultural and vegetation features from high resolution geo-referenced mass points produced from both airborne and ground based laser sensors. The various investigative research tasks that need to be performed include: 1. Evaluate the various airborne and ground based laser systems to determine the most effective operational use of each respective technology or combination of technologies to achieve the highest level of integration and exploitation with hyperspectral signatures. 2. Establish tools to effectively combine hyperspectral signatures with the optimal resolution of laser derived mass point data. 3. Conduct a series of benchmark tests to develop processing filters and utilities to effectively compare and contrast both cultural and vegetation hyperspectral signatures with laser derived mass point data. 4. Explore the use of hyperspectral imagery to assist in enhancing and discriminating the effects of vegetation features on laser elevation data. The researchers evaluations should preferably include hands-on valuations from multiple laser-derived elevation and hyperspectral data sets during the Phase I development. PHASE II: Will accumulate the processing capabilities that are defined in Phase I into a prototype system. The prototype system will further develop and apply these emerging processing capabilities to support a broad range of military and civil engineering applications. The utilization and integration of laser-derived elevation and hyperspectral data for these applications will result in the production of the next-generation class of end products suitable for use in a broad spectrum of applications; from execution of combat engineering tasks, to providing timely information for the management of environmentally sensitive areas within military installations. PHASE III: This SBIR would result in a dual use technology with broad applications in addressing environmental issues in the civil community. The result of these combined technologies would provide Federal, State, and Local resource managers with more comprehensive information on land conditions to make informed land management decisions, and would directly support conservation measures in highly sensitive habitats. The combination of the hyperspectral technology with the laser geo-referenced elevation data would further advance the civil communities ability to construct more detailed census of individual species that require detailed estimates of vegetative cover. These remote sensing functionalities would also provide data critical to resource managers, who need successive collections conducted over time to produce information on changing land conditions and composition. This SBIR further addresses the research issues associated with developing the next generation of processing techniques that will be required to effectively combine and differentiate these two rapidly advancing remote sensing technologies and provide an unprecedented level of source data input for the rapid construction and attribution of terrestrial regions outside and with in urban areas which could directly support military battlespace requirements and objectives. REFERENCES: 1) Integration of Lidar and Landsat ETM Data, A.T. Hudak, M.A. Lefsky, and W.B. Cohen ASPRS Land Surface Mapping and Characterization Using Laser Altinetry Workshop, Annapolis, Maryland, October 2001, Session 4, Data Fusion. http://lvis.gsfc.nasa.gov/proceedings/s4/session4.pdf 2) Investigation of Measuring Accuracy of Forest Area by Means of Airborne Laser Scanner, M. Funahashi, M. Setojima, Y. Akamatsu ASPRS Land Surface Mapping and Characterization Using Laser Altinetry Workshop, Annapolis, Maryland, October 2001, Session 6, Forestry Applications. http://lvis.gsfc.nasa.gov/proceedings/s6/session6.pdf KEYWORDS: Airborne and Ground Based Laser Systems, Hyperspectral, Elevation Data, Mass Points. A02-136 TITLE: Hardened, Fast Response Thermal Measurement TECHNOLOGY AREAS: Materials/Processes OBJECTIVE: To design, develop, and fabricate a fast response thermal measurement system for the measurement of temperature time histories (gases and detonation products) inside the fireball produced by high explosive and incendiary devices. This system should be capable of measuring temperatures from 0 to 4000 degrees Celsius with a rise time of 1 millisecond or less. Shock pressures in the region of interest could be as high as 1000 psi. DESCRIPTION: Survivability and Protective Structures research as well as research in many other technical areas involves experimental analysis of high temperatures associated with the fireball produced by high explosive and incendiary devices. Acceptable techniques include standoff systems as well as hardened reusable transducers. Current visual techniques are limited by their framing rate as well as obscuration by dust and smoke produced during energetic events. Thermocouples are limited by their response time and fragility in the fireball region. A measurement technique is needed which overcomes these limitations. PHASE I: Conduct a feasibility investigation, select the most appropriate concepts, and prepare a preliminary design. The feasibility investigation must include at least two alternatives that address the criteria described above. The investigation must include a comparison analysis of alternatives considered. A preliminary design should be prepared. This initial design should include as a minimum: specifications on components (composition and properties) and assembly (operational capabilities), interface and control software, estimated cost, packaging/transportability, and estimated component size and weight(s). PHASE II: Prepare final design and construct full-scale prototype(s). Samples must be prepared to evaluate performance in weapons effects experiments. Full-scale system must be constructed to evaluate construction requirements, efficiency, packaging, and overall system performance. Refine the prototype system as deemed necessary from results of laboratory and field trials. Documentation should be prepared to define construction tactics, techniques, and procedures, and fully describe applications and limitations of the developed systems PHASE III: This technology has the potential to benefit any process or application where transient temperatures with extremely fast rise times occur. As well as blast effects research, various manufacturing processes, and engine monitoring applications come to mind. This should be a commercially viable technology for both civilian and military customers. REFERENCES: 1) Joachim, Charles E., KA-III, Phase C, M-1 Propellant Tests: Deflagration in Partial Confinement 2) Knox, E. C., Improved Techniques for Measuring Thermal Effects of Propellant Burn Tests in Confined Areas. 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