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Accession Number
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ADA564903
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Title
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Multi-Sensor Data Fusion: An Unscented Least Squares Approach.
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Publication Date
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Jul 2011
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Media Count
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9p
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Personal Author
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J. George L. M. Kaplan
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Abstract
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This manuscript provides an approach to solving the nonlinear least squares problem that arises in decentralized fusion. In decentralized fusion, measurements are first processed at the sensor node before they are relayed to the central node. Even though almost all sensor noise can be modeled as additive noise, the additive nature of the measurement noise is lost when the signal is processed at the sensor node. The proposed unscented transformation- based approach helps to tackle the non-additive nature of the noise in the nonlinear least squares problem. Numerical simulations indicate that the proposed unscented transformation-based approach yields desired results.
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Keywords
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Approximation(Mathematics)
Computerized simulation
Cost functions
Data fusion
Decentralized data fusion
Estimates
Gaussian noise
Information fusion
Iterative least squares
Least squares method
Measurement
Measurement noise
Multiple gunfire detection systems
Multisensors
Nodes
Nonlinear least squares problem
Sensor fusion
Signal processing
Sniper localization
Symposia
Taylors series
Transformations(Mathematics)
Unscented transformation approximation
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Source Agency
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Non Paid ADAS
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NTIS Subject Category
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72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
62 - Computers, Control & Information Theory
63F - Optical Detection
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Corporate Author
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Army Research Lab., Adelphi, MD.
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Document Type
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Technical report
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Title Note
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Conference paper.
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NTIS Issue Number
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1304
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Contract Number
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N/A
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