We present a method for deriving an automatic target recognition (ATR) system using geospatial features and a Data
Model populated decision architecture in the form of a self-organizing knowledge base. The goal is to derive an ATR
that recognizes targets it has seen before while minimizing false alarms (zero false alarms). We present an investigation
of the performance of analytical Data Models as a sensor and data fusion process for automatic target recognition (ATR),
and summarize results including on a 2 km background run where no false alarms were encountered.
We present a series of algorithms and preliminary work towards developing a fully autonomous and real-time lightning
severity prediction capability enabling one hour ahead forecasting based on local lightning strike characteristics. Our
approach characterizes total, cloud to cloud, and cloud to ground strikes as input variables to derive the number of strikes
Matching journal entries to appropriate context responses can be a daunting problem, especially when there are no salient
keyword matches between the entry and the proposed library of appropriate responses. We examine a real-world
application for matching interactive journaling requests for guidance to an a priori established archive of sufficient
multimedia responses. We show the analysis required to enable a Data Model based algorithm to group journaling entries
according to intrinsic context information and type. We demonstrate a new lookup table (LUT) classifier that exploits all
available data in LUT form.
We present a simple approach for deriving ensembles of training data from notional belief networks. This is accomplished by specifying the belief variable interactions in the form of Bayes expert system or directed graph, where the node conditional and prior probabilities are specified heuristically from data or from subject matter expert (SME) heuristics. The resulting network is then sampled across parameter space and the associated input/output pairs retained for deriving a principal component Data Model using regression techniques. The method is general and the details of the algorithm are presented.
Providing a flexible and reliable source of IR target imagery is absolutely essential for operation of an IR Scene Projector in a hardware-in-the-loop simulation environment. The Kinetic Kill Vehicle Hardware-in-the-Loop Simulator (KHILS) at Eglin AFB provides the capability, and requisite interfaces, to supply target IR imagery to its Wideband IR Scene Projector (WISP) from three separate sources at frame rates ranging from 30 - 120 Hz. Video can be input from a VCR source at the conventional 30 Hz frame rate. Pre-canned digital imagery and test patterns can be downloaded into stored memory from the host processor and played back as individual still frames or movie sequences up to a 120 Hz frame rate. Dynamic real-time imagery to the KHILS WISP projector system, at a 120 Hz frame rate, can be provided from a Silicon Graphics Onyx computer system normally used for generation of digital IR imagery through a custom CSA-built interface which is available for either the SGI/DVP or SGI/DD02 interface port. The primary focus of this paper is to describe our technical approach and experience in the development of this unique SGI computer and WISP projector interface.