A method is described to improve the performance of sensorfusion algorithms. Data sets available for training fusion algorithms are often smaller than desired, since the sensor suite used for data acquisition is alwa...
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(纸本)081943664X
A method is described to improve the performance of sensorfusion algorithms. Data sets available for training fusion algorithms are often smaller than desired, since the sensor suite used for data acquisition is always limited by the slowest, least reliable sensor. In addition, the fusion process expands the dimension of the data, which increases the requirement for training data. By using structural risk minimization, a technique of statistical learning theory, a classifier of optimal complexity can be obtained, leading to improved performance. A technique for jointly optimizing the local decision thresholds is also described for hard-decision fusion. The procedure is demonstrated for EMI, GPR and MWIR data acquired at the US Army mine lanes at Fort A.P. Hill, VA, Site 71A. It is shown that fusion of features, soft decisions, and hard decisions each yield improved performance with respect to the individual sensors. fusion decreases the overall error rate (false alarms and missed detections) from roughly 20% for the best single sensor to roughly 10% for the best fused result.
The main issue considered is how to configure a sensor control system for fighter-based ESA-oriented multifunction and multisensor capability. The main structure should be simple, robust and stable, but it is also imp...
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The main issue considered is how to configure a sensor control system for fighter-based ESA-oriented multifunction and multisensor capability. The main structure should be simple, robust and stable, but it is also important to consider the relationship between fine sensor parameters and the large scaled sensor allocation mechanism. An important ontology to tracking and sensor management is given in [1]. Control of complex sensor control mechanisms, especially when an electronically steered array antenna (ESA) is present, requires however applicable software architectures. Two proposals will be given for that, agent modelling and multilevel sensor management architecture. Agent modelling may help to keep the principles of system clean, simple and robust, while the multi-level system architecture will help to organize the relationship between distributed processes in the system. For example, several sensor-oriented tracking processes may be used for the same target, but also other process types may be distributed, such as situation awareness and sensor management. Multilevel architectures will also facilitate a subdivision of the manufacturing process between different subcontractors.
Specular reflections from environments cause uncertainties to ultrasonic sensor range data. In this paper, we examine the application of evidential method for data integration using the specially designed sensor model...
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Specular reflections from environments cause uncertainties to ultrasonic sensor range data. In this paper, we examine the application of evidential method for data integration using the specially designed sensor model to overcome the problem. Dempster's rule of combination is used to fuse the sensor data to obtain the map defined on a 2D evidence grid. The sensor model tries to reduce the uncertainties caused by specular reflections with a filtering factor. Experimental results have shown the usefulness of this method.
Bayesian and Dempster-Shafer Theory based methods are among the alternative algorithmic approaches to multisensor data fusion. The two approaches differ significantly and the extent of their applicability to data fusi...
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Bayesian and Dempster-Shafer Theory based methods are among the alternative algorithmic approaches to multisensor data fusion. The two approaches differ significantly and the extent of their applicability to data fusion is still being debated. This paper presents a Monte Carlo simulation approach for a comparative analysis of a Dempster-Shafer Theory based and a Bayesian multisensor data fusion in the classification task domain, including the implementation of both formalisms, and the results of the Monte Carlo experiments of this analysis.
The present paper explores the dynamic level of information sensory fusion which is to be appropriate for hardware implementations. We associate to multitracking sensors their abstractions, being discrete time multihe...
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The present paper explores the dynamic level of information sensory fusion which is to be appropriate for hardware implementations. We associate to multitracking sensors their abstractions, being discrete time multihead state circuits. We presume sensors are to be independent from each other and there are no direct interfaces between them. The fusion is achieved by sensor-to-sensor track association which is controlled by the global state transition system. We investigate synchronous and asynchronous fusion models over common and distributed resource spaces and we compare the recognition capacities of these and some other models, like Turing Machines, stack automata etc. Then the fusioned circuits are applied to analyze arithmetical predicates, social games and an unsolved `Syracuse Conjecture'.
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.
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