A method for vehicle navigation, comprising: obtaining satellite positioning data from a satellite positioning device of a vehicle; obtaining vehicle sensor data from a number of sensors of the vehicle; and combining the satellite positioning data and the vehicle sensor data by a Kalman filter to obtain a combined state vector estimate of the vehicle; wherein the Kalman filter comprises - a first filter which receives the satellite positioning data, but not the vehicle sensor data, and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, - a second filter which receives the vehicle sensor data, but not the satellite positioning data, and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and - a third filter which receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and, from the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix, generates the combined state vector estimate and a corresponding combined state error covariance matrix, wherein the third filter comprises a prediction processor which generates a predicted state vector estimate on the basis of the combined state vector estimate and a predicted state error covariance matrix on the basis of the combined state error covariance matrix, wherein the predicted state vector estimate and the predicted state error covariance matrix are fed back to the first filter, the second filter, and the third filter, wherein the first filter determines the first state vector estimate by updating the fed back predicted state vector estimate with the received satellite positioning data, and wherein the second filter determines the second state vector estimate by updating the fed back predicted state vector estimate with the received vehicle sensor data. obtaining satellite positioning data from a satellite positioning device of a vehicle; obtaining vehicle sensor data from a number of sensors of the vehicle; and combining the satellite positioning data and the vehicle sensor data by a Kalman filter to obtain a combined state vector estimate of the vehicle; wherein the Kalman filter comprises - a first filter which receives the satellite positioning data, but not the vehicle sensor data, and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, - a second filter which receives the vehicle sensor data, but not the satellite positioning data, and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and - a third filter which receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and, from the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix, generates the combined state vector estimate and a corresponding combined state error covariance matrix, obtaining vehicle sensor data from a number of sensors of the vehicle; and combining the satellite positioning data and the vehicle sensor data by a Kalman filter to obtain a combined state vector estimate of the vehicle; wherein the Kalman filter comprises - a first filter which receives the satellite positioning data, but not the vehicle sensor data, and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, - a second filter which receives the vehicle sensor data, but not the satellite positioning data, and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and - a third filter which receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and, from the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix, generates the combined state vector estimate and a corresponding combined state error covariance matrix, - a first filter which receives the satellite positioning data, but not the vehicle sensor data, and generates a first state vector estimate of the vehicle and a corresponding first state error covariance matrix, - a second filter which receives the vehicle sensor data, but not the satellite positioning data, and generates a second state vector estimate of the vehicle and a corresponding second state error covariance matrix, and - a third filter which receives the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix and, from the first state vector estimate, the first state error covariance matrix, the second state vector estimate, and the second state error covariance matrix, generates the combined state vector estimate and a corresponding combined state error covariance matrix,