Multi-sensor Information Fusion and Strong Tracking Filter for Vehicle Nonlinear State Estimation

According to the problem that some key state parameters in vehicle stability control process are too difficult to directly measure, combining the strong tracking filtering theory with data fusion estimation technology, and by a 4-DOF nonlinear vehicle dynamics model, the algorithm of multi-sensor linear combination state optimization estimation based on strong tracking filter is proposed. For the multi-sensor and signals model nonlinear dynamic systems having the same sample rates for each sensor, the fusion estimate on the basis of global information by use of strong tracking filter is established, and the effectiveness of the new algorithm is also illustrated by use of an example. The result show that the states of vehicle stability control system can be estimated accurately and low costs with this algorithm.