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Accession Number ADA564903
Title Multi-Sensor Data Fusion: An Unscented Least Squares Approach.
Publication Date Jul 2011
Media Count 9p
Personal Author J. George L. M. Kaplan
Abstract 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.
Keywords Approximation(Mathematics)
Computerized simulation
Cost functions
Data fusion
Decentralized data fusion
Gaussian noise
Information fusion
Iterative least squares
Least squares method
Measurement noise
Multiple gunfire detection systems
Nonlinear least squares problem
Sensor fusion
Signal processing
Sniper localization
Taylors series
Unscented transformation approximation

Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
62 - Computers, Control & Information Theory
63F - Optical Detection
Corporate Author Army Research Lab., Adelphi, MD.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1304
Contract Number N/A

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