Practical mechanisms to realize smooth transitions for unconventional multi-sensor integrated kinematic positioning and navigation

Conventionally, the Kalman filter on the basis of integration mechanization, such as GPS-aided inertial integrated navigation system, has been commonly built up using error states and error measurements. In order to accurately reflect the evolution of the real state for a moving vehicle, we adopted an unconventional KF that directly estimated navigational parameters instead of the error states, in which a kinematic trajectory model as the main part of KF system model was deployed and measurement updates for all sensor data inclusive of the ones from IMUs were directly performed. To best describe the trajectory instead of applying the most complex model throughout, this research proposed relevant practical mechanisms for linear motion and circular motion to realize smooth transitions between alternative kinematic models. Experiments and simulations were tested to show the practicability of the proposed practical approach.