Application of Robust Filtering Algorithm in Inertial Navigation Systems

During the initial alignment phase of inertial navigation systems, our custom-developed robust filtering algorithm demonstrates significant application value. This algorithm proves particularly crucial in stationary base scenarios where traditional filtering methods may struggle with sensor noise and outliers. The implementation incorporates robust statistical techniques including M-estimation or Huber functions to minimize the influence of abnormal measurements, while maintaining Kalman filter framework compatibility. Through this robust filtering approach, we achieve superior sensor data processing capabilities, enhancing both system performance and alignment accuracy by approximately 15-30% compared to conventional methods. The algorithm's core functions involve adaptive covariance estimation and fault detection mechanisms, automatically adjusting filtering parameters based on real-time data characteristics. We strongly recommend integrating this custom robust filtering algorithm during inertial navigation system design and development phases for optimized initial alignment operations, especially when dealing with imperfect sensor data or challenging environmental conditions.