Strapdown Inertial Navigation System Static Base Alignment Fine Alignment Kalman Filter Simulation

Inertial Navigation System (INS) fine alignment under static base conditions represents a classic state estimation problem, where Kalman filtering is employed to optimize multi-sensor data fusion. In static environments, the system primarily addresses precise initial attitude determination without interference from linear or angular motion, facilitating effective separation of sensor errors. The core of fine alignment lies in establishing an appropriate Kalman filter model. Typically, an error state model is adopted, incorporating inertial device errors (gyroscope biases, accelerometer biases, and misalignment angles) as state variables. The filter progressively corrects attitude errors by comparing inertial computation results with reference measurements of gravity and Earth rotation rates. Under static conditions, measurement equations can be simplified to projection relationships of gravity vectors and Earth rotation vectors in the body coordinate system. Key considerations for simulation implementation include: State equation design - Dynamic models encompassing attitude errors, velocity errors, position errors, and inertial instrument errors Measurement update strategy - Primary reliance on accelerometer-measured gravity vectors and gyroscope-measured Earth rotation components during static alignment Noise parameter configuration - Process noise and measurement noise covariance matrices directly impact filter convergence performance Observability analysis - Certain states may be unobservable under static conditions, necessitating appropriate model simplification The fine alignment process typically exhibits two-phase characteristics: rapid initial convergence followed by gradual optimization. Simulation enables evaluation of how different algorithm parameters affect convergence speed and steady-state accuracy, providing theoretical foundations for practical system调试. Code Implementation Notes: - State vector typically includes [attitude errors, velocity errors, position errors, gyro biases, accelerometer biases] - Measurement innovation calculated as difference between INS-derived gravity/earth rate and reference values - Discrete Kalman filter implementation requires proper discretization of continuous system models - Tuning parameters include Q (process noise) and R (measurement noise) covariance matrices - Convergence criteria often based on covariance matrix trace or state estimate stability