The most recent client-server version of Pythia modeling software is presented. Pythia is a software implementation of a
Bayesian Net framework and is used for course of action development, evaluation, and selection in the context of
effects-based planning. A new version, Pythia 1.5, is a part of a larger suite of tools for behavioral influence analysis,
brought into the state-of-the-art client-server computing environment. This server application for multi-user and multiprocess
computing relies on the Citrix Presentation Server for integration, security and maintenance. While Pythia's
process is run on a server, the input/output services are controlled and displayed through a client PC. Example use of
Pythia is illustrated through its application to a suppression of IED activity in an Iraqi province. This case study
demonstrates how analysts can create executable (probabilistic) models that link potential actions to effects, based on
knowledge about the cultural and social environment. Both the tool and the process for creating and analyzing the
model are described as well as the benefits of using the new server based version of the tool.
Current mine detection research indicates that no single sensor or single look from a sensor will detect mines/minefields in a real-time manner at a performance level suitable for a forward maneuver unit. Hence, the integrated development of detectors and fusion algorithms are of primary importance. A problem in this development process has been the evaluation of these algorithms with relatively small data sets, leading to anecdotal and frequently over trained results. These anecdotal results are often unreliable and conflicting among various sensors and algorithms. Consequently, the physical phenomena that ought to be exploited and the performance benefits of this exploitation are often ambiguous. The Army RDECOM CERDEC Night Vision Laboratory and Electron Sensors Directorate has collected large amounts of multisensor data such that statistically significant evaluations of detection and fusion algorithms can be obtained. Even with these large data sets care must be taken in algorithm design and data processing to achieve statistically significant performance results for combined detectors and fusion algorithms. This paper discusses statistically significant detection and combined multilook fusion results for the Ellipse Detector (ED) and the Piecewise Level Fusion Algorithm (PLFA). These statistically significant performance results are characterized by ROC curves that have been obtained through processing this multilook data for the high resolution SAR data of the Veridian X-Band radar. We discuss the implications of these results on mine detection and the importance of statistical significance, sample size, ground truth, and algorithm design in performance evaluation.
The ATR community has a strong and growing interest in ATR systems that adapt to changing circumstances and is developing means to solve these dynamic and difficult ATR problems. To facilitate this research, the AFRL COMPASE and SDMS organizations have developed an AdaptSAPS framework for developing and assessing such adaptive ATR systems. This framework, in the form of AdaptSAPS Version 1.0, provides MATLAB code, organized procedures, and an organized database for adaptive ATR systems.
SAIC is applying their Ellipse Detector (ED) to this framework to validate the AdaptSAPS procedures and to test the AdaptSAPS database. The ED previously has shown utility on a variety of sensors and ATR problems. Although computationally efficient, the ED is more complex and much more powerful than simpler detectors such as a two parameter CFAR. However, the ED is not currently implemented as an adaptive ATR.
In this paper, we show the utility of the AdaptSAPS framework for developing and assessing a non-trivial adaptive ATR by embedding the SAIC ED in the AdaptSAPS framework. We point out the strong points and weak points of AdaptSAPS Version 1.0 and recommend enhancements for future versions. In particular, we comment on AdaptSAPS as delivered, the current missions and data bases in AdaptSAPS, and the current performance measures in AdaptSAPS.
Current research in minefield detection indicates that operationally no single sensor technology will likely be capable of detecting mines/minefields in a real-time manner and at a performance level suitable for a forward maneuver unit. Minefield detection involves a particularly wide range of operating scenarios and environmental conditions, which requires deployment of complementary sensor suites. Consequently, the NVESD sponsored Signal Processing and Algorithm Development for Robust Mine Detection (SPAD) Program is currently focusing on the development of computationally efficient and robust detection algorithms applicable to a variety of sensors and on the development of a robust decision level fusion algorithm that exploits these detectors. One SPAD detection technique, called the Ellipse Detector, has been previously reported in the open literature. We briefly report on the continued robust performance of this detector on some new sensor output. We also report on another robust detector developed for sensors that produce output not suitable for the Ellipse Detector. However, the focus of this paper is on the SPAD decision level fusion algorithm, called the Piecewise Level Fusion Algorithm (PLFA). We emphasize the robustness and flexibility of the PLFA architecture by describing its performance and results for both multisensor and multilook fusion.
The acquisition a robust set of IR imagery is frequently impossible through the traditional image collection process. On the other hand, the use of a full-scale simulation is too time consuming and frequently produces unrealistic images. Therefore, other methods are sought that would exploit a small subset of sample real-world images for rapid database prototyping. This paper presents a fast and simple method of high-resolution target image insertion into a low-resolution image of a terrain. The method exploits a naive physics paradigm. First, a high-resolution target image is diffused using a Gaussian kernel and on-target zooming effect. A target binary mask guides the diffusion process. The diffused image is re-sampled onto a low-resolution target image. Next, a down-sampled target image is inserted into a given terrain image using two target insertion/diffusion processes and additional effects. These diffusion processes eliminate contrasts at the border area of a target and on the terrain/background. Background-to-target diffusion extends the heat of overlapped terrain pixels over a target section. Target-to-background diffusion radiates and overlaps target heat over the border area of the adjacent section of the terrain. Developed processes mirror the physics of heat propagation and diffusion, and apply weighted pixel mixing to eliminate target insertion contrasts. Given a set of high-resolution turntable data and a set of terrain images, a training database can be generated within a short time. The number of parameters controlling the insertion process has been decreased to the minimum and brought into a control panel. Each parameter has understandable physical meaning and has assigned a meaningful range of values.
Proc. SPIE. 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI
KEYWORDS: Target detection, Detection and tracking algorithms, Sensors, Image sensors, Signal processing, Laser Doppler velocimetry, Mining, Algorithm development, Land mines, General packet radio service
Current minefield detection research indicates that operationally no single sensor technology will likely be capable of detecting mines/minefields in a real-time manner and at a performance level suitable for a forward maneuver unit. Minefield detection involves a particularly wide range of operating scenarios and environmental conditions, which requires deployment of complementary sensor suites. We have focused, therefore, on the development of a computationally efficient and robust detection algorithm that exploits robust image processing techniques centered on meaningful target feature sets applicable to a variety of imaging sensors. This paper presents the detection technique, emphasizing its robust architecture, and provides performance results for image data generated by complementary sensors. The paper also briefly discusses the application of this detector as a component of fusion architectures for processing returns form diverse imaging sensors, including multi-channel image data from disparate sensors.
The paper presents a model evolution methodology for object Recognition under dynamic perceptual conditions. The methodology consists of a Model Application, a Model Evolution, and a Reinforcement Learning. The model application is an approach to the recognition of objects within a sequence of images, which have been acquired under dynamic perceptual conditions. In this approach an RBF (Radial Basis Function)- based classifier is applied to classify/segment objects within each image. The model evolution is concerned with the modification of models, which are created off-line or continue to be updated on-line. The purpose of the model evolution is that these models can adapt to next incoming images. The model evolution is achieved with the help of the reinforcement learning, which is activated to generate information for model evolution, when it is needed to modify models according to perceived disparities between the models and reality. The methodology has been achieved through the development of an adaptive vision system, which consists of three main subsystems: Model Application system, Reinforcement Learning system, and Model Evolution system. They have been developed and integrated in a close-loop so that object models can evolve to recognize objects under variable perceptual conditions.
The paper presents a methodology and GETP experimental system for rapid SAR target signature generation from limited initial sensory data. The methodology exploits and integrates the following four processes: (1) analysis of initial SAR image signatures and their transformation into higher-level blob representation, (2) blob modeling, (3) genetic inheritance modeling to generate new instances of a target model in blob representation, and (4) synthesis of new SAR signatures from genetically evolved blob data. The GETP system takes several SAR signatures of the target and transforms each signature into more general scattered blob graphs, where each blob represents local energy cluster. A single graph node is describe by blob relative position, confidence, and iconic data. Graph data is forwarded to the genetic modeling process while blob image is stored in a catalog. Genetic inheritance is applied to the initial population of graph data. New graph models of the target are generated and evaluated. Selected graph variations are forwarded to the synthesis process. The synthesis process restores target signature from a given graph and a catalog of blobs. The background is synthesized to complement the signature. Initial experimental results are illustrated with 64 X 32 image sections of a tank.
In this paper, we discuss a new fusion architecture, including some preliminary results on field data. The architecture consists of a new decision level fusion algorithm, the piecewise level fusion algorithm (PLFA), integrated with a new expert system based user assistant that adjusts PLFA parameters to optimize for a user desired classification performance. This architecture is applicable for both multisensor and multilook fusion. The user specifies classification performance by inputting entries for a desired confusion matrix at the fusion center. The intelligent assistant suggests input alternatives to reach the performance goal based on previously supplied user inputs and on performance specifications of the individual sensors. If deadlock results, i.e., the goal is not attainable because of conflicting user inputs, the assistant will inform the user. As the user and assistant interact, the assistant calculates the parameters necessary to automatically adjust the PLFA for the required performance. These parameters and calculations are hidden from the user. That is, the architecture is designed so that user inputs are intuitive for an unskilled operator. The implementation of this adaptable fusion architecture is due to the relatively simple structure of the PLFA and the expert system heuristic rules. We briefly describe the PLFA structure and operation, illustrate some expert system rules, and discuss preliminary performance of the entire architecture, including a sample dialogue between the user and the intelligent assistant. We conclude this paper with a discussion of future extensions to this architecture that include replacing human interactions with dynamic learning techniques.