Multi-sensor Information Fusion In The Vehicle Positioning And Navigation Applications

As infrastructure, road traffic transport system has very important role that supports people's daily life and economy. But, ceaseless happening traffic accidents, continual traffic jam, air polluting and environment destroying that caused by traffic development gradually become very serious society problems. Intelligent transportation system (ITS) is considered a very promising way to resolve traffic problems, ITS is a general modern science and technology designation that contains a series of advanced technology and technology integration system that increase the efficiency of traffic system transport and enhance road safety. Developing ITS can improve transport system appearance, promote correlation industry, apply production of science and technology and protect environment.The key problem of ITS is keeping highway clear. Vehicle navigation system (VNS) is an important application project of solving traffic jam that takes more and more attention. But four problems exist in VNS, in which vehicle location precision and reliability and speediness, veracity and credibility of data communication especially stand out. With federal Kalman filtering, vehicle location precision is studied, an fusion algorithm is derived and optimization of system is proved. For speediness of data extract, wavelet estimation and decomposition of signals based on Kalman filtering technique algorithm is introduced. The algorithm performs the estimation and decomposition simultaneously, using the discrete wavelet transform implemented by filter banks. It is developed based on the standard Kalman filtering scheme, and hence preserves the merits of the Kalman filer for signal estimation in the sense that it produces an optimal estimate of the unknown signal in a recursive manner.