MATLAB Implementation of Strapdown Inertial Navigation Algorithm

Strapdown Inertial Navigation Systems (SINS) represent one of the core technologies in modern navigation, which calculate attitude and position information in real-time using inertial measurement units mounted directly on the vehicle. Implementing this algorithm in MATLAB requires understanding three key components: sensor data preprocessing, attitude updating, and position updating.

The first step involves processing raw data from gyroscopes and accelerometers. Gyroscopes output angular velocity information of the vehicle, while accelerometers measure specific force. These raw measurements typically contain noise and errors, necessitating filtering and compensation processing using techniques like Kalman filtering or moving average algorithms implemented through MATLAB's signal processing toolbox functions.

Attitude updating forms the critical core of the navigation solution. Common algorithms include quaternion methods and direction cosine matrix methods, with quaternion methods widely adopted due to their computational efficiency. By integrating gyroscope angular velocity data combined with initial attitude from alignment procedures, the vehicle's roll, pitch, and yaw angles can be updated in real-time. MATLAB implementation typically involves using quaternion multiplication and normalization functions to maintain numerical stability during continuous integration.

Position updating relies on attitude information and accelerometer data. After transforming specific force measurements from the body frame to navigation frame using the current attitude matrix, double integration yields position changes. This process must account for Earth's rotation and gravity effects, typically using local geographic coordinate systems as the navigation frame. MATLAB code implementation requires careful handling of coordinate transformations using rotation matrix operations and gravity compensation models based on geographic location.

When implementing in MATLAB, attention must be paid to numerical integration algorithm selection, with Euler's method and Runge-Kutta methods being commonly used. The ode45 function in MATLAB provides an efficient implementation of adaptive Runge-Kutta integration. Furthermore, since integration processes accumulate errors, practical applications require integrated navigation systems combining other navigation sources, often implemented through sensor fusion algorithms like Kalman filters available in MATLAB's Navigation Toolbox.