INS/GPS Loose Integration Program with Kalman Filter for Integrated Navigation

This article discusses the implementation of a Kalman filter program for INS/GPS loose integration in integrated navigation systems. To better understand this program, we first need to grasp the concept of integrated navigation. Integrated navigation is an approach that utilizes multiple sensors for navigation, including accelerometers, gyroscopes, magnetometers, GPS receivers, and others. INS (Inertial Navigation System) and GPS (Global Positioning System) are two commonly used sensors that can operate cooperatively in different environments to provide more precise navigation information. However, due to measurement errors in both INS and GPS, along with signal transmission delays and other issues, using them independently may lead to deviations in navigation results. Therefore, we employ integrated navigation methods to fuse data from multiple sensors, thereby obtaining more accurate navigation information. In this process, the Kalman filter plays a crucial role by optimizing and correcting sensor data to enhance navigation accuracy. The implementation typically involves two main stages: prediction and update. During the prediction phase, the system uses INS data to propagate the state vector (position, velocity, attitude) based on kinematic models. The update phase then incorporates GPS measurements to correct the predicted states. Key implementation aspects include: - State vector design containing position, velocity, attitude errors, and sensor biases - Measurement models for GPS position and velocity updates - Covariance matrix management for error estimation - Tuning of process and measurement noise matrices - Handling of asynchronous sensor data through time synchronization algorithms