Strapdown Inertial Navigation System Initial Alignment Program

Initial alignment of the strapdown inertial navigation system (SINS) is a critical process during system startup, aimed at determining the initial attitude relationship between the body frame and navigation frame. Depending on the carrier's state, initial alignment can be classified into stationary base alignment and moving base alignment methods.

Stationary Base Alignment Stationary base alignment applies when the carrier is stationary, primarily using measurements of gravity vector and Earth rotation angular velocity vector to determine initial attitude. Typical implementation involves two phases: coarse alignment and fine alignment. In the coarse alignment phase, accelerometer and gyroscope outputs are processed to compute the initial attitude matrix through vector observations. The fine alignment phase employs Kalman filtering or other optimization methods to further improve alignment accuracy by estimating error states.

Moving Base Alignment Moving base alignment is used when initial alignment must be performed while the carrier is in motion, typically requiring additional information assistance. Common methods include velocity matching and position matching. Velocity matching estimates attitude errors by comparing the velocity output from the INS with external reference velocities (such as GPS). Position matching utilizes position information to assist alignment, suitable for extended motion scenarios. Implementation often involves data fusion algorithms like complementary filtering or extended Kalman filters.

MATLAB implementation typically involves these key steps: sensor data preprocessing (including noise filtering and calibration), coarse alignment algorithm implementation (using trigonometric functions for attitude calculation), fine alignment filter design (implementing Kalman filter with state vectors for attitude errors), and performance evaluation through Monte Carlo simulations. For moving base alignment, integration of external reference information and data fusion algorithms becomes crucial, requiring careful handling of asynchronous data streams.

Regardless of stationary or moving base alignment, the accuracy and speed of initial alignment directly impact subsequent navigation solution accuracy. Therefore, program design must comprehensively consider factors such as algorithm complexity, real-time performance, and environmental adaptability, often involving trade-off studies between computational efficiency and alignment precision.