Research And Implementation Of Kalman Filtering Algorithm In GPS/INS System

ITS (Intelligent Transport system) is a huge complex opening system in an emerging interdisciplinary research area. In ITS, advanced information processing, communications, auto-control and computer engineering are synthetically applied in a whole transport management system to establish a precise and high efficient real time system. One important aspect in ITS is the vehicle navigation system which is found on global positioning system, geographic information system, database search system and GSM system. In recent years, it has become important in the field of vehicle navigation system about how to integrate GPS technology with Inertial Navigation System.This paper first introduces the basic principles of GPS. And then this paper focuses on how to use Kalman filtering to estimate various states of GPS/INS system, and how the estimates of states could regulate the system to make sure GPS/INS system is more reliable on the occasion of bad signals receiving by information fusion method. The basic idea of the Kalman filtering algorithm is:Establishing the space model of signal states and noise states. Using estimates of the previous moment and observations at present to update the estimation of state variables, find the estimates at present. It is a recursive estimation algorithm, the best estimates of the physical parameters are obtained by processing a series of actual measurement data with errors. The biggest feature is the ability to remove random noise in order to gain useful information which is close to the real situation. At last, compare the proposed algorithm with existing algorithms. The track processed by Kalman filtering algorithm is much smoother than the original track, and the precision of the position is higher. The continuity of the track is also assured when GPS is interrupted.