Application of Kalman Filter Algorithm in Motion Base Initial Alignment

In the initial alignment process of inertial navigation systems, we can utilize our self-developed Kalman filter algorithm to enhance system accuracy. The algorithm implements recursive prediction and correction cycles through multiple computations on sensor data, effectively reducing measurement errors and compensating for disturbances caused by moving platforms. The implementation typically involves state-space modeling with process and measurement equations, where the filter recursively estimates system states while minimizing mean-squared estimation errors. Key functions include state prediction using system dynamics models, covariance propagation, Kalman gain calculation, and state update based on sensor measurements. Beyond inertial navigation systems, the Kalman filter algorithm finds extensive applications in various domains such as robotic navigation, aerospace engineering, and autonomous vehicle control. Therefore, gaining profound understanding and mastering this algorithm holds significant importance for improving technical capabilities and solving practical engineering problems.