Abstract We construct the hyperbolic curve model and then use the least square method to obtain its parameters. These parameters are regarded as state vectors to contain dynamic noises to erect a Kalman filter model based on the hyperbolic curve model. On the basis of this model we forecast settling amounts of the building. Since the parameters of the Kalman filter model change continuously, its ability to suit the observation data is increased, and the fitting error of the model is reduced. An example of calculation shows that the forecast error is small, and this suggests that it is best to use the Kalman filter model based on the hyperbolic curve model to forecast settling amounts of the building.
LU Fumin,JIANG Tingyao. Application of Kalman Filter Method Based on Hyperbolic Curve Model in the Settlement Forecast of Building[J]. jgg, 2016, 36(6): 517-.
LU Fumin,JIANG Tingyao. Application of Kalman Filter Method Based on Hyperbolic Curve Model in the Settlement Forecast of Building[J]. jgg, 2016, 36(6): 517-.