Initial Alignment for Inertial Navigation Systems: NED Frame Implementation with 10-State Kalman Filter

In inertial navigation systems, initial alignment represents a critical foundational task. To ensure navigation accuracy and system stability, the alignment process must be implemented using the North-East-Down (NED) coordinate frame and involves estimation of 10 key state variables. The state vector typically includes accelerometer biases, gyroscope drifts, and installation misalignment errors between inertial sensors. In code implementation, this is commonly structured as a Kalman filter problem where the state transition matrix models the system dynamics while measurement updates incorporate reference data. During initial alignment procedures, calibration must be performed under stationary conditions or during slow uniform motion to minimize dynamic interference. The algorithm typically involves two phases: coarse alignment using analytical methods for initial attitude estimation, followed by fine alignment employing optimal estimation techniques. This dual-stage approach ensures rapid convergence while maintaining high precision. The complexity of initial alignment necessitates meticulous computational handling and careful sensor calibration. Proper implementation requires considerations for Earth rotation compensation, gravitational field modeling, and sensor error characteristics. Successful alignment establishes the foundation for reliable navigation performance, making this process indispensable for high-accuracy inertial navigation applications. Code implementation often features modular design with separate functions for coordinate transformation, sensor error modeling, and filter iteration cycles.