Kalman Filter Simulation for Fine Alignment of Strapdown Inertial Navigation System on Static Base

This paper presents the development of a Kalman filter simulation program for evaluating fine alignment accuracy in strapdown inertial navigation systems. The program implementation involves critical algorithms including inertial sensor calibration, quaternion-based attitude determination, and Kalman filtering techniques for state estimation. During the development process, I encountered several challenges related to algorithm convergence and computational efficiency, which were resolved through iterative parameter tuning and optimization of matrix operations.

Furthermore, the paper examines practical limitations and issues in strapdown inertial navigation system applications. The simulation evaluates how static base alignment accuracy impacts overall system performance, with proposed enhancements such as adaptive filtering approaches and improved sensor error modeling. The code structure implements modular components for sensor data preprocessing, state prediction, and measurement updates using optimal filtering techniques.

Finally, potential research directions are discussed to better understand and utilize strapdown inertial navigation systems, including non-linear filtering implementations and multi-sensor fusion architectures. The simulation framework provides a foundation for future investigations into alignment optimization and error compensation methods.