The Geomagnetic Filtering Algorithm Based on Correlative Probability Density Add-Weight

To overcome the linearizated errors from the state equation and observation equation based on Extended Kalman Filter (EKF), a Unscented Kalman Filter (UKF) matching algorithm using geomagnetic anomaly based on probability weighted was proposed. For the problem that the quasi observations might be arose by choosing the same weight coefficient as the UT transformation, the geomagnetic anomaly UKF filtering algorithm associated with probability density function to assign weight for the sampled observation has been researched. The two experiments have been carried out in the South China Sea from the Earth Magnetic Anomaly Grid 2 (EMAG2), it is shown that the problem mentioned above could be overcome based on probability weighted, the drifting errors of inertial navigation system in longitude and latitude can be reduced by the modified algorithm, and the positioning accuracy and reliability of the modified algorithm is obviously superior to that of the traditional UKF algorithm and the Inertial Navigation System (INS).