| 摘要 |
A data fusion architecture with a plurality of sensors, optionally position measuring equipment (PMEs), is described. Each sensor supplies measurement data x1, x2 . . . xM and is associated with accuracy data H1, H2 . . . HM indicative of the accuracy of the supplied measurement data. Sub-processing units derives first estimates sf1, sf2 . . . sfM and second estimates Hn1, Hn2 . . . HnM of the variability of the measurement data supplied by the respective sensor. The first estimates are derived by processing the measurement data x1, x2 . . . xM and the second estimates are derived by processing the accuracy data H1, H2 . . . HM. The first and second estimates are combined in a multiplier to derive overall estimates sigma1, sigma2 . . . sigmaM of the variability of the measurement data supplied by the respective sensor. Data fusion means such as a Kalman filter combines the measurement data supplied by each sensor and the overall estimates sigma1, sigma2 . . . sigmaM for each sensor. |
