Kalman Filter for Integrated Navigation

In ancient times, people navigated by stars and compasses. With technological advances, inertial navigation and satellite navigation such as GPS are used for positioning. Multi-sensor fusion technology combines different navigation data under optimal fusion criteria, generating various integrated navigation schemes. Typical examples include FOG strapdown INS and low-cost MEMS/MOEMS gyro strapdown INS integrated with GPS, GLONASS and BeiDou.

The core of integrated navigation lies in information fusion algorithms, which combine inertial navigation and satellite navigation data to obtain more accurate position, velocity and attitude results.

Since the 1950s, researchers have explored data fusion between multiple navigation systems. Kalman proposed a linear filter later named Kalman Filter (KF). It fuses position and velocity solved by IMU with satellite navigation outputs, filters measurement errors and provides optimal estimates of position, velocity and attitude. The principle is recursive: it uses current observations and previous filtered estimates to continuously update the optimal navigation state variables.

Kalman Filter and its derivative filters are widely adopted in integrated navigation. Extended Kalman Filter (EKF) is developed for nonlinear systems. Adaptive Kalman Filter is introduced to handle unknown or time-varying noise environments.