Abstract: A navigation system to transition from a stationary alignment filter to an in-motion alignment filter is provided. The system comprises a processing unit configured to implement a stationary alignment Kalman filter (SAKF) in gyrocompass alignment mode to generate state estimates and provide corrections when the object is stationary, and to implement an algorithm to compute a covariance for the SAKF that accounts for uncertainty in the SAKF estimates; wherein the processing unit is further configured to implement a continuous alignment filter (CAF) that generates a secondary solution which remains unaffected by the SAKF corrections during a delay period accommodating a delay between the time of actual motion to the time of detected motion, and to implement an algorithm to compute a covariance for CAF that accounts for the uncertainty in CAF during delay period; and wherein outputs of the CAF and its covariance are communicated to an in-motion alignment filter.

Abstract: A navigation system to transition from a stationary alignment filter to an in-motion alignment filter is provided. The system comprises a processing unit configured to implement a stationary alignment Kalman filter (SAKF) in gyrocompass alignment mode to generate state estimates and provide corrections when the object is stationary, and to implement an algorithm to compute a covariance for the SAKF that accounts for uncertainty in the SAKF estimates; wherein the processing unit is further configured to implement a continuous alignment filter (CAF) that generates a secondary solution which remains unaffected by the SAKF corrections during a delay period accommodating a delay between the time of actual motion to the time of detected motion, and to implement an algorithm to compute a covariance for CAF that accounts for the uncertainty in CAF during delay period; and wherein outputs of the CAF and its covariance are communicated to an in-motion alignment filter.