GPS and Inertial Navigation Integrated Navigation System

The integrated navigation system combining GPS and inertial navigation is a widely adopted approach that leverages the complementary advantages of both systems to achieve high-precision positioning. The GPS system provides absolute position information but is susceptible to signal obstructions, while the inertial navigation system operates independently of external signals with high short-term accuracy but suffers from cumulative errors over time. The core of integrated navigation lies in data fusion algorithms, typically implemented using Kalman filtering, which optimally combines the outputs from both systems. This implementation often involves state-space modeling where system states (position, velocity, attitude) are estimated through prediction and correction cycles. The Kalman filter algorithm continuously weights GPS measurements against inertial sensor data (accelerometers and gyroscopes) to minimize estimation errors. This solution ensures long-term stability while maintaining short-term high precision, making it extensively applied in aviation, marine navigation, and autonomous driving systems. Key functions in the implementation include sensor data preprocessing, coordinate transformation, and real-time filter tuning to adapt to dynamic environmental conditions.