{
  "$type": "site.standard.document",
  "description": "The present invention provides a data fusion architecture with a plurality of sensors (4 1 , 4 2 ... 4 M ), optionally position measuring equipment (PMEs). Each sensor supplies measurement data x 1 , x 2 ...x M and is associated with accuracy data H 1 , H 2 ...H M indicative of the accuracy of the…",
  "path": "/patents/980456",
  "publishedAt": "2013-12-25T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G01B21/16",
    "GE ENERGY POWER CONVERSION TECHNOLOGY LTD [GB]"
  ],
  "textContent": "The present invention provides a data fusion architecture with a plurality of sensors (4 1 , 4 2 ... 4 M ), optionally position measuring equipment (PMEs). Each sensor supplies measurement data x 1 , x 2 ...x M and is associated with accuracy data H 1 , H 2 ...H M indicative of the accuracy of the supplied measurement data. Sub-processing units (6 1 , 6 2 ...6 M ) derives first estimates sf 1 , sf 2 ...sf M and second estimates Hn 1 , Hn 2 ...Hn M of the variability of the measurement data supplied by the respective sensor. The first estimates are derived by processing the measurement data Ç 1 , Ç 2 ...Ç M and the second estimates are derived by processing the accuracy data H 1 , H 2 ...H M . The first and second estimates are combined in a multiplier (16 1 , 16 2 ... 16 M ) to derive overall estimates à 1 , à 2 ... à M of the variability of the measurement data supplied by the respective sensor. Data fusion means such as a Kalman filter (2) combines the measurement data supplied by each sensor (4 1 , 4 2 ...4 M ) and the overall estimates à 1 , à 2 ... à M for each sensor.",
  "title": "Data fusion architecture"
}