GPS/INS Integrated Navigation Filtering Algorithm

In modern navigation systems, GPS (Global Positioning System) serves as a critical technology for determining position, velocity, and time. However, GPS reliability can be compromised by various factors including weather conditions and environmental interference. To enhance navigation system accuracy and robustness, researchers have developed GPS/INS integrated navigation filtering algorithms. These algorithms typically implement Kalman filtering techniques (such as Extended Kalman Filters or Unscented Kalman Filters) that fuse inertial navigation system (INS) data with GPS signals to achieve superior positioning and velocity information. The implementation generally involves:

1. Sensor data preprocessing and synchronization
2. State prediction using INS mechanization equations
3. Measurement update through GPS observations
4. Real-time error estimation and compensation

This integrated approach effectively mitigates GPS signal errors and INS drift accumulation, significantly improving navigation system reliability. Consequently, GPS/INS integrated navigation filtering algorithms have become essential technologies widely deployed in aviation, marine navigation, vehicle guidance systems, and other modern navigation applications.