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Multi-Sensor Data Fusion

Multi-Sensor Data Fusion . H.B. Mitchell. Three-hour tutorial on multi-sensor data fusion The tutorial is closely based on a selection of material taken from the book: Multi-Sensor Data Fusion: An Introduction by H.B. Mitchell published by Springer-Verlag (2007). Introduction Sensors

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Multi-Sensor Data Fusion

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  1. Multi-Sensor Data Fusion H.B. Mitchell UNCLASSIFIED

  2. Three-hour tutorial on multi-sensor data fusion The tutorial is closely based on a selection of material taken from the book: Multi-Sensor Data Fusion: An Introduction by H.B. Mitchell published by Springer-Verlag (2007) Introduction Sensors Common Representational Format Spatial, Temporal, Semantic Alignment Robust Statistics Ensemble Learning Multi-Sensor Data Fusion UNCLASSIFIED

  3. Introduction UNCLASSIFIED

  4. Data fusion: Theory and Techniques which combine sensor data into a common representational format. Aim is to improve the quality of information. Data fusion is analogous to the manner in which humans and animals improve their chances of survival by exploiting their Man-Made Fusion System EO, IR, Radar A priori information and/or historical information Bayesian inference, fuzzy logic, Dempster-Shaefer Multi-Sensor Data Fusion Multiple senses Experience Ability to reason UNCLASSIFIED

  5. Multi-sensor data fusion brings together many different techniques and applications Medical Imaging Remote Sensing Surveillance Data Mining Computer Vision Stereo Imaging Multi-Disciplinary Subject Techniques Computing Power Bayesian networks Signal Processing Statistical Estimation Tracking Algorithms Classification Algorithms Invariant Subspaces MCMC Genetic Algorithms Bagging, Boosting Applications Fusion UNCLASSIFIED

  6. Complementary Fusion After. Toet. Natural color mapping for multiand nightvision imagery. Information Fusion (2003) UNCLASSIFIED

  7. Pan Sharpening Pan-sharpened image Panochromatic image Multi-spectral image UNCLASSIFIED

  8. Colorization After. Toet. Natural color mapping for multiband nightvision iamgery. Information Fusion (2003) UNCLASSIFIED

  9. Sensors UNCLASSIFIED

  10. Sensors are devices which interact directly with environment Sensors are the source of all input data. Often use smart sensors which Transform sensor signal to standardized digital format Calibrates sensor signal Transmits sensor signal via standardized interfaces. Sensors transmitter/ sensor receiver element Filter A/D m Amp UNCLASSIFIED

  11. Build a Data Fusion System as a distributed assembly of fusion nodes Fusion Node Input Data Fusion Output Data Aux Inform. Ext. Inform UNCLASSIFIED

  12. Logical sensor is any device which functions as a source of inform. for a multi-sensor data fusion node S1: Physical sensor F1: Virtual Sensor: A fusion node whose output is fed into another fusion node F1 F2 S1 “virtual” sensor S2 S3 Logical Sensor UNCLASSIFIED

  13. Sensor Errors Sensors only give an estimate of the measured physical property Nature of errors often determine the preferred fusion algorithm Bias. Separately track with each set of measurements, then fuse tracks. No Bias. Concatenate measurements into one vector then track with Kalman Filter UNCLASSIFIED

  14. Common Representational Format UNCLASSIFIED

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