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sensorfus.pdf
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🔗 Source: stat.cmu.edu
📊 Size: 311 KB
📄 Pages: 1 page
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Summary

Kalman Filter and Sensor Fusion are equivalent to Constrained Regression. The Kalman Filter is a two-step process for sequential estimation, while Sensor Fusion can be seen as a regression of measurements on a state, with a possible recovery of state dynamics. The contributions of this work include the equivalence of the Kalman Filter to augmented Sensor Fusion, and Sensor Fusion to regression with constraints. Extensions of this work include the use of penalties for covariance shrinkage and sensor selection, as well as gradient boosting for joint sensor fitting. An application of Sensor Fusion is shown in influenza nowcasting, where it is used to predict weekly flu incidence in 51 US states using 308 sensors with digital surveillance sources. The results show that Sensor Fusion with L2 penalty outperforms other methods in terms of mean absolute error.

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Description

Kalman Filter and Sensor Fusion are equivalent to Constrained Regression.

Technical Information

  • File Format: PDF
  • File Size: 311 KB
  • Pages: 1
  • Language: EN
  • Total Downloads: 336
  • Last Updated: 1 week ago

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