Distance Estimation using Intelligent Fusion of Navigation Data

Abstract

INS/GPS navigation has changed due to advances in inertial manufacturing technology. This has created sensors (accelerometers, gyroscopes, magnetometers) that can be packaged with a GPS receiver/antenna for everyday use. These new manufacturing technologies are creating inertial sensors such as MEMS inertial sensors that are smaller and consume less power, but they also exhibit much larger errors comparing with their higher priced units. Kalman filtering is the main technique for combined IMU/GPS sensor data processing. Intelligent algorithm application for sensor data fusing is one of the newest areas of research. In the current paper the simplified intelligent algorithm is developed for GPS and accelerometer data combined processing. The performance of developed algorithm was checked through made experiments for estimation of distance passed by vehicle. The compare of passed distance estimation error for standard and intelligent Kalman algorithm are presented in the paper. Presented intelligent Kalman algorithm has smaller estimation error, simple in use and not demanding for computing power.