A carrier-phase DGPS based V2V object sensing system using fast incremental Bayesian network

This paper describes a novel approach to moving-baseline carrier-phase differential GPS (DGPS) and a vehicle-to-vehicle object system based on the approach. In order to achieve sub-decimeter level accuracy, a Bayesian network is proposed to fuse measurements from GPS and low-cost vehicle dynamic sensors for trajectory reconstruction. A fast recursive implementation whose complexity scales linearly with the tra- jectory length is derived. Experimental results are presented to illustrate the approach´s effectiveness to fuse data from GPS and vehicle dynamic sensors. To show the performance and effectiveness of the proposed vehicle-to-vehicle (V2V) object sensing system, we choose a frequency-modulated continuous wave (FMCW) radar as the benchmark for comparison.