Multi Sensor Data Fusion for Land Vehicle Navigation

Abstract

Navigation is a tool, which has gained popularity for land vehicles in recent years. However, achieving navigation with high accuracy is still a desire to be accomplished. In pursuit of achieving this goal in a low cost budget multi-sensor data fusion has been seen as a promising solution. The Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in the recent decade. The estimation accuracy of a lowcost

inertial navigation system (INS) is limited by the accuracy of the used sensors and the imperfect mathematical modelling of the error sources. By fusing the INS data with GPS data, the errors can be bounded and the accuracy increases considerably. For fusion of data and minimization of error a Kalman filter based approach has been developed in this work. The work puts forward a cost benefit solution for achieving accurate navigation through sensor fusion.