MATLAB Implementation of Integrated Navigation Systems

Integrated navigation is a technique that combines information from multiple navigation systems, typically integrating Inertial Navigation Systems (INS) with Global Positioning Systems (GPS) to enhance navigation accuracy and reliability. Implementing integrated navigation in MATLAB primarily involves Dead Reckoning (DR) and sensor data fusion algorithms.

The fundamental principle of dead reckoning methods utilizes acceleration and angular velocity data from Inertial Measurement Units (IMU) to calculate position and attitude through numerical integration. The implementation steps include:

Data Preprocessing First, filter and calibrate raw IMU data to eliminate noise and bias errors. Common preprocessing methods include low-pass filtering or moving average filters, implemented using MATLAB's `filter` function or custom smoothing algorithms.

Attitude Calculation Compute vehicle attitude angles (pitch, roll, yaw) using gyroscope-measured angular velocities. Common algorithms include quaternion methods or Euler angle approaches. The quaternion method offers higher computational efficiency and avoids gimbal lock issues. MATLAB's `quaternion` class or aerospace toolbox functions like `angle2quat` can be utilized for implementation.

Velocity and Position Update Based on accelerometer data combined with attitude information, transform measurements from the body frame to navigation frame using coordinate transformation matrices, then perform numerical integration to obtain velocity and position. Note that integration cumulative errors increase over time, requiring external correction (e.g., GPS). MATLAB's `cumtrapz` function can be used for numerical integration operations.

Integrated Navigation Algorithm Typically employs Kalman Filter (KF) or Extended Kalman Filter (EKF) to fuse IMU and GPS data. KF reduces errors through optimal estimation, improving navigation accuracy. In MATLAB, developers can use built-in functions like `kalman` or manually implement state equations and observation equations using matrix operations and recursive algorithms.

Error Compensation During GPS signal loss, the system relies purely on inertial navigation, causing rapid error accumulation. Techniques like Zero-Velocity Update (ZUPT) or map matching can be applied for compensation. These can be implemented through conditional statements checking velocity thresholds or map database comparisons.

When writing integrated navigation programs in MATLAB, developers can use `Simulink` for modular design or implement functional modules using scripts. The final program should include real-time data acquisition, algorithm computation, and visualization capabilities using MATLAB's plotting functions, facilitating analysis and debugging.