Effect of In-Drilling Alignment with General Dynamic Error Model on Azimuth Estimation

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

“…At present, the research about north-seeker includes basic inertial navigation error model [ 1 , 2 ], observability analysis [ 3 , 4 ], in-drilling alignment with general dynamic error model [ 5 ], alignment algorithms based on gyro-compassing mode [ 6 , 7 ], alignment based on the interacting multiple model and the Huber methods [ 8 ], rapid fine alignment under marine mooring condition [ 9 ], alignment for SINS (strapdown inertial navigation system) in vehicular environment [ 10 ], alignment based on Riccati Equation and EM (expectation-maximization) convergence [ 11 ], alignment based on adjustment on separate-bias Kalman filter [ 12 ], application of nonlinear filtering in alignment [ 13 ], initial attitude estimation of tactical grade inertial measurement [ 14 ], alignment with robust adaptive unscented Kalman filter [ 15 ], alignment based on a group of double direct spatial isometries [ 16 ], alignment with state-dependent extended Kalman filter [ 17 ], application of redundant technology in north-finding [ 18 ], two-position algorithm [ 19 , 20 ], multi-position algorithm [ 21 , 22 ], rotary-modulation algorithm [ 23 , 24 , 25 ], nonlinear filter model for large misalignment angle [ 26 ], transfer north-seeking algorithm [ 27 , 28 , 29 ], transfer alignment based on cubature Kalman filter (CKF) method [ 30 ], north-finding based on the neural network technology [ 31 , 32 ], transfer algorithm based on sensors network and estimation of wing flexure deformation [ 33 ], fast stationary initial alignment based on extended measurement information [ 34 ], accurate fine alignment based on adaptive extended Kalman filters [ 35 ], compact north-seeker technology [ 36 ], north-seeking based on the information fusion technology [ ...…”