GPS/INS Loosely Coupled Integration MATLAB Simulation Source Code

Integrated navigation systems play a vital role in modern navigation applications, particularly when GPS signals are limited or temporarily unavailable. In such scenarios, Inertial Navigation Systems (INS) can provide continuous navigation information. Loosely Coupled Integration represents a common integration approach that enhances positioning accuracy and reliability by fusing GPS and INS data.

This shared MATLAB simulation source code implements a GPS/INS loosely coupled navigation system. The core methodology employs Kalman Filter (KF) algorithms to integrate data from both systems. GPS provides absolute position and velocity information, while INS measures vehicle motion states through accelerometers and gyroscopes. The distinctive feature of loosely coupled integration is that both systems operate independently, with data fusion occurring only at a higher level, thereby reducing system coupling complexity.

The simulation workflow includes: Simulating GPS and INS sensor data with consideration of noise and error models. Designing Kalman filter prediction and update stages to process GPS and INS observation data. Evaluating integrated navigation system performance by comparing error variations between standalone INS and integrated navigation approaches.

This simulation serves as a valuable reference for navigation algorithm researchers and engineers. By adjusting filter parameters or sensor models, users can further optimize integrated navigation accuracy. Additionally, the loosely coupled architecture is more straightforward to implement compared to tightly coupled integration, making it an ideal introductory case for studying integrated navigation systems. The code implementation includes key functions for sensor data generation, Kalman filter initialization, state prediction, measurement updates, and performance metrics calculation.