Strapdown Inertial Navigation System (SINS) and GPS Integrated Navigation

The integration of Strapdown Inertial Navigation System (SINS) and Global Positioning System (GPS) represents a common solution in modern navigation applications. SINS relies on Inertial Measurement Units (IMU) to provide continuous attitude, velocity, and position data, but suffers from error accumulation over time. While GPS offers high accuracy, its performance degrades due to signal obstructions or interference. By combining both systems, their complementary advantages enhance overall navigation accuracy and reliability.

Implementing this integrated navigation system in MATLAB typically involves designing data fusion algorithms. Adaptive algorithms play a crucial role in this process by dynamically adjusting filter parameters based on environmental changes to optimize navigation performance. For instance, when GPS signals are strong, the system can rely more heavily on GPS data through proper weighting in the Kalman filter implementation. During GPS signal loss or degradation, the adaptive algorithm automatically shifts weighting coefficients to prioritize SINS data, thereby minimizing error accumulation. Key MATLAB functions often include kalmanFilter for state estimation and adaptive tuning mechanisms for real-time parameter adjustment.

Experimental results demonstrate that SINS/GPS integrated navigation systems employing adaptive algorithms significantly improve navigation accuracy, particularly in challenging environments like urban canyons or tunnels, where they exhibit enhanced robustness. This approach shows promising application prospects in autonomous vehicles, UAV navigation, and other fields requiring reliable positioning solutions.