While exact methods (e.g., jump-diffusion algorithms) for performing maximum a posteriori (MAP) target detection and recognition can be very complex and computationally expensive, it is often not clear how to develop ...
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While exact methods (e.g., jump-diffusion algorithms) for performing maximum a posteriori (MAP) target detection and recognition can be very complex and computationally expensive, it is often not clear how to develop effective and less complex suboptimal methods. Also, MAP algorithms typically generate hard decisions, but for fusionapplications it would often be more desirable to have probabilities or confidence levels for a range of alternatives. In this paper, we consider the application of a framework called probability propagation in Bayesian networks. This framework organizes computations for iterated approximations to posterior probabilities, and has been used recently by communications researchers to derive very effective iterative decoding algorithm. In this paper, we develop a Bayesian network model for the problem of target detection and recognition, and use it in conjunction with Markov models for target regions to derive a probability propagation algorithm for estimating target shape and label probabilities.
We consider the problem of recognizing M objects using a fusion center with N parallel sensors. Unlike conventional M-ary decision fusion systems, our fusion system breaks a complex M-ary decision fusion problem into ...
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We consider the problem of recognizing M objects using a fusion center with N parallel sensors. Unlike conventional M-ary decision fusion systems, our fusion system breaks a complex M-ary decision fusion problem into a sequence of simpler binary decision fusion problems. In our system, a binary decision tree (BDT) is employed to hierarchically partition the object space at all system elements. The traversal of the BDT is synchronized by the fusion center. The sensor observations are assumed conditionally independent given the unknown object type. We use a greedy performance criterion in which the probability of error is minimized at individual nodes. Using this performance criterion, we characterize the optimal fusion rules and the optimal sensor rules. We compare our results with some important results on conventional one-stage binary fusion.
This paper describes development and testing of a program that provides a quantitative metric for the comparison of night vision fusion algorithms. The user enters into the Metric Program the names of a thermal file, ...
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This paper describes development and testing of a program that provides a quantitative metric for the comparison of night vision fusion algorithms. The user enters into the Metric Program the names of a thermal file, a vision file and the corresponding fused image file. The program assigns a fusion rating to the algorithm based on the following four quantitative tests: information content (ic), vision retention (vr), thermal retention (tr), and the bar test to detect black segments. In ic the information content of the fused image is compared with a weighted sum of the vision and thermal images. In vr the number of faint lights that the fused image failed to incorporate is counted. In tr the number of pixels from the thermal file included in the fused image is determined. With some fusion algorithms if one of the sensors is blocked, a black segment appears in that area in the fused image, thus losing the information from the unblocked sensor. To test for this the Metric Program creates a thermal file with three horizontal black bars. The program then allows the user to call the executable file of the algorithm under test. Then the user is asked to examine the fused image. If three pitch-black horizontal bars appear on the image, the algorithm fails the test. While the bar test is invariant to the vision/thermal image pair used, the other tests are not. For this reason it is suggested that an algorithm should be tested with 5 or 6 different image pairs and a mean fusion rating calculated. The program is used to evaluate several different algorithms. Day vision fusion algorithms are also tested.
In assessing a fused sensor system, one considers the quality of the system architecture most often by the capabilities of the individual sensors, and the attributes of the fusion algorithm. Though it is possible to e...
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This work addresses the often neglected, but important problem of Level 3 fusion or threat refinement. This paper describes algorithms for threat prediction and test results from a prototype threat prediction fusion e...
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This work addresses the often neglected, but important problem of Level 3 fusion or threat refinement. This paper describes algorithms for threat prediction and test results from a prototype threat prediction fusion engine. The threat prediction fusion engine selectively models important aspects of the battlespace state using probability-based methods and information obtained from lower level fusion engines. Our approach uses hidden Markov models (HMMs) of a hierarchical threat state to find the most likely Course of Action (CoA) for the opposing forces. Decision trees use features derived from the CoA probabilities and other information to estimate the level of threat presented by the opposing forces. This approach provides the user with several measures associated with the level of threat, including: probability that the enemy is following a particular CoA, potential threat presented by the opposing forces, and likely time of the threat. The hierarchical approach used for modeling helps us efficiently represent the battlespace with a structure that permits scaling the models to larger scenarios without adding prohibitive computational costs or sacrificing model fidelity.
The Pacific Northwest National Laboratory is involved in the design and development of algorithms to improve feature identification and detection using multisensor imagery. This research is funded jointly by the Natio...
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The Pacific Northwest National Laboratory is involved in the design and development of algorithms to improve feature identification and detection using multisensor imagery. This research is funded jointly by the National Imagery and Mapping Agency (NIMA) and the U.S. Department of Energy. A process has been designed that exploits the spatial discontinuities in a scene as revealed by the reflectance variation in a given frequency. We believe that by mapping the discontinuities in a scene, man-made objects can be better distinguished from natural objects. The process involves the generation of a texture map for each of the multisensor data sets;this facilitates the fusion of data from different sources with different physical characteristics. The advantage of this approach is that texture seems to reduce image data to a common base. This common base becomes important when using data of variable quality, resolution, and geometry. Texture analysis has applicability to a wide variety of feature identification and extraction applications. This paper focus on demonstrating how the classification of texture maps derived from multisensor imagery can be used to automatically extract major roads from multisensor imagery, a requirement from NIMA under its comprehensive and integrated geospatial information generation strategy. Automatic/assisted road extraction is a particularly challenging task given the need for global coverage, accurate positioning, and sophisticated attribution.
In this paper we offer an overview of design principles and propose a fusion process reference model that provides guidance for the design of data fusion systems. We incorporate a formal method approach to fusion syst...
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In this paper we offer an overview of design principles and propose a fusion process reference model that provides guidance for the design of data fusion systems. We incorporate a formal method approach to fusion system design and show the role of the psychology of the human/computer interface in the system design process. Data fusion is a complex, multi-faceted field that has evolved from a number of different disciplines. This disparate nature has lead to a largely bottom-up approach to data fusion system design where the components are constructed first and the system-level issues addressed afterwards. The result is an ad hoc, prototype driven philosophy which, we content, is neither efficient nor effective. We believe that design of data fusion systems needs to be given proper consideration, with a top-down approach that addresses system-level constraints first, thereby offering the possibility of re-usable, abstract structures. We offer an object-centred model of data fusion together with practical tools for studying and refining the model so that it can be useful in designing real data fusion systems.
A decision support tool has been developed that advises on approach and algorithm selection for automated data analysis systems. These approaches and algorithms include the standard data and information fusion methods...
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A decision support tool has been developed that advises on approach and algorithm selection for automated data analysis systems. These approaches and algorithms include the standard data and information fusion methods. The tool comprises a database of fuzzy rules in disjunctive normal form. These rules were obtained by eliciting heuristic knowledge from established practitioners of data fusion. The input to the system consists of a variety of problem characteristics, some of which are fuzzy quantities and others are crisp values. Where fuzzy granulation was required this again was elicited from experts. The final fuzzy rule based system has been implemented as a Windows executable called Equity, which is freely available to download from the World Wide Web.
In past presentations, in the book Mathematics of Data fusion, and in the recent monograph An Introduction to Multisource-Multitarget Statistics and Its applications, we have shown how Finite-Set Statistics" (FIS...
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ISBN:
(纸本)081943678X
In past presentations, in the book Mathematics of Data fusion, and in the recent monograph An Introduction to Multisource-Multitarget Statistics and Its applications, we have shown how Finite-Set Statistics" (FISST) provides a unified foundation for the following aspects of multisource-multitarget data fusion: detection, identification, tracking, multi-evidence accrual, sensor management, performance estimation, and decision-making. In this paper we apply FISST to the distributed fusion problem: i.e., fusing the outputs produced by geographically separated data fusion systems. We propose two different approaches: optimal (assuming that correlations are completely known) and robust (assuming that correlations are completely unknown). Optimal distributed fusion is achieved via a direct FISST multitarget generalization of the Chong-Mori-Chang single-target track-fusion technique. Robust distributed fusion is achieved by using FISST to generalize the Uhlmann-Julier Covariance Intersection (CI) method to the multitarget case.
Multiple model fusion is useful in applications in which the model of the signal processes is not known with certainty. This paper compares two current fusion algorithms with a novel alternative. The new fusion approa...
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ISBN:
(纸本)0780365143
Multiple model fusion is useful in applications in which the model of the signal processes is not known with certainty. This paper compares two current fusion algorithms with a novel alternative. The new fusion approach is shown to give improved performance when the observation rate is slow as compared with the important time constants of the signal.
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