A new track-to-track association algorithm mixing kinematics data provided by the radar and identification data provided by the Electronic Support Measure (ESM) sensor is presented. The performance of this algorithm i...
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A new track-to-track association algorithm mixing kinematics data provided by the radar and identification data provided by the Electronic Support Measure (ESM) sensor is presented. The performance of this algorithm is confirmed in terms of probability of correct association and probability of false association. This algorithm provides the double advantage of providing information about the common origin of the tracks and an identification of each track.
The Joint Directors of Laboratories (JDL) Data fusion Group's Data fusion Model is the most widely used method for categorizing data fusion-related functions. This model is modified to facilitate the cost-effectiv...
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The Joint Directors of Laboratories (JDL) Data fusion Group's Data fusion Model is the most widely used method for categorizing data fusion-related functions. This model is modified to facilitate the cost-effective development, acquisition, integration and operation of multi-sensor/multi-source systems. Proposed modifications include broadening of the functional model and related taxonomy beyond the original military focus, and integrating the Data fusion Tree Architecture model for system description, design and development.
In this paper we present a methodology for fuzzy sensorfusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) it aggregates ...
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ISBN:
(纸本)0819431931
In this paper we present a methodology for fuzzy sensorfusion. We then apply this methodology to sensor data from a gas turbine power plant. The developed fusion algorithm tackles several problems: 1.) it aggregates redundant (but uncertain) sensor information;this allows making decisions which sensors land to what degree) should be considered for propagation of sensor information. 2.) It filters out noise and sensor failure from measurements;this allows a system to operate despite temporary or permanent failure of one or more sensors. For the fusion, we use a combination of direct and functional redundancy. The fusion algorithm uses confidence values obtained for each sensor reading from validation curves and performs a weighted average fusion. With increasing distance from the predicted value, readings are discounted through a non-linear validation function. They are assigned a confidence value accordingly. The predicted value in the described algorithm is obtained through application of a fuzzy exponential weighted moving average time series predictor with adaptive coefficients. Experiments on real data from a gas turbine power plant show the robustness of the fusion algorithm which leads to smooth controller input values.
We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and n...
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We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and noisy three-band color image example.
This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect...
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ISBN:
(纸本)0819431931
This paper describes a preliminary approach to the fusion of multi-spectral image data for the analysis of cervical cancer. The long-term goal of this research is to define spectral signatures and automatically detect cancer cell structures. The approach combines a multi-spectral microscope with an image analysis tool suite, MathWeb. The tool suite incorporates a concurrent Principal Component Transform (PCT) that is used to fuse the multi-spectral data. This paper describes the general approach and the concurrent PCT algorithm. The algorithm is evaluated from both the perspective of image quality and performance scalability.
The Dempster Shafer (DS) Theory of Evidential Reasoning may be useful in handling issues associated with theater ballistic missile discrimination. This paper highlights the Dempster-Shafer theory and describes how thi...
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The Dempster Shafer (DS) Theory of Evidential Reasoning may be useful in handling issues associated with theater ballistic missile discrimination. This paper highlights the Dempster-Shafer theory and describes how this technique was implemented and applied to data collected by two infrared sensors on a recent flight test.
The Common Object Request Broker Architecture (COBRA) has been proven to be effective for application in the Data fusion domain. However, the benefits of this system have not yet been fully realized because of unresol...
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The Common Object Request Broker Architecture (COBRA) has been proven to be effective for application in the Data fusion domain. However, the benefits of this system have not yet been fully realized because of unresolved issues concerning reliability, fault-tolerance and real-time/fast enough QoS behavior of the system. In view of this, an attempt has been made to develop a domain specific environment with the commercially available standard products. The result is a COBRA based infrastructure (CORBIS) that provide interfaces and mechanisms for various applications and services.
Availability of different imaging modalities requires techniques to process and combine information from different images of the same phenomena. We present a symmetry based approach for combining information from mult...
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Availability of different imaging modalities requires techniques to process and combine information from different images of the same phenomena. We present a symmetry based approach for combining information from multiple images. fusion is performed at data level. Actual object boundaries and shape descriptors are recovered directly from raw sensor output(s). Method is applicable to arbitrary number of images in arbitrary dimension.
The potential problem of deterioration in recognition system performance because of imprecise, incomplete, or imperfect training is a serious challenge inherent to most real-world applications. This problem is often r...
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The potential problem of deterioration in recognition system performance because of imprecise, incomplete, or imperfect training is a serious challenge inherent to most real-world applications. This problem is often referred to in certain applications as degradation of performance under off-nominal conditions. This study presents the results of an investigation carried out to illustrate the scope and benefits of information fusion in such off-nominal scenarios. The research covers features in - decision out (FEI-DEO) fusion as well as decisions in - decision out (DEI-DEO) fusion. The latter spans across both information sources (sensors) and multiple processing tools (classifiers). The investigation delineates the corresponding fusion benefit domains using as an example, real-world data from an audio-visual system for the recognition of French oral vowels embedded in various levels of acoustical noise.
The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensorfusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor i...
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ISBN:
(纸本)0819431931
The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensorfusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI. The first demonstration of the FLIPS concept targets interceptor functionality;other applications, mainly in robotics and autonomous systems are considered for the future. The main assumption behind ELIPS is that fuzzy, rule-based and neural forms of computation can serve as the main primitives of an "intelligent" processor. Thus, in the same way classic processors are designed to optimize the hardware implementation of a set of fundamental operations, ELIPS is developed as an efficient implementation of computational intelligence primitives, and relies on a set of fuzzy set, fuzzy inference and neural modules, built in programmable analog hardware. The hardware programmability allows the processor to reconfigure into different machines, taking the most efficient hardware implementation during each phase of information processing. Following software demonstrations on several interceptor data three important ELIPS building blocks (a fuzzy set preprocessor, a rule-based fuzzy system and a neural network) have been fabricated in analog VLSI hardware and demonstrated microsecond-processing times.
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