Complex Strapdown Inertial Navigation Systems

Strapdown Inertial Navigation Systems (SINS) represent an autonomous navigation technology based on Inertial Measurement Units (IMUs). Compared to platform-based inertial navigation systems, SINS features simpler mechanical structure but significantly higher algorithmic complexity. In complex scenarios, the system requires fusion of attitude, position, and velocity information to achieve high-precision navigation, with core challenges lying in real-time multi-source data processing and error compensation algorithms.

Attitude calculation serves as the foundation of SINS, typically implemented through gyroscope-measured angular velocity combined with quaternion or direction cosine matrix updates for carrier attitude determination. Since gyroscopes suffer from drift, complementary filtering or Kalman filtering techniques are employed to fuse accelerometer and magnetometer data for long-term error correction. Code implementation often involves quaternion normalization and matrix orthogonalization procedures to maintain numerical stability.

Position and velocity updates rely on specific force measurements from accelerometers. After transforming specific force to the navigation coordinate system using attitude matrices, velocity and position are obtained through integration following gravity compensation and Coriolis force correction. However, integration processes accumulate errors, particularly with low-precision sensors, necessitating Zero-Velocity Updates (ZUPT) or GNSS assistance algorithms. Implementation requires careful handling of numerical integration methods and coordinate transformation routines.

Sensor fusion constitutes the key to accuracy enhancement. Multi-source information (e.g., GNSS, odometry) is integrated with inertial data through Extended Kalman Filters (EKF) or factor graph optimization to suppress drift. Advanced error models incorporating temperature effects and scale factors further optimize navigation performance in complex dynamic environments. Code architecture typically involves modular design for different sensor interfaces and fusion algorithms.

In practical applications, algorithms must balance real-time performance and precision. For high-dynamic carriers like UAVs, rapid-update attitude algorithms are essential, while long-duration missions such as marine navigation require focused solutions for error accumulation problems. Implementation considerations include algorithm optimization for specific hardware capabilities and environmental conditions.