Abstract:

In the existing Gussian particle filter (GPF), sample importance density function is constructed through combining the latest measurement based on the Gaussian filter (GF).However, the true posterior probability density could be approximated badly by the GF under the condition of high accuracy, strong nonlinearity measurements.In order to solve this problem, a truncated adaptive cubature kalman filter is proposed, based on which a new sample importance density function is constructed, so that a truncated adaptive cubature particle filtering method can be derived.Simulation results show that the proposed filtering algorithm has higher estimation accuracy than the existing GPF for addressing the nonlinear state estimation with high accuracy and strong nonlinearity measurements.