Abstract:

To solve the nonlinear problem in pure bearing tracking, a range parametrization rootmeansquare cubature Kalman filter is proposed to eliminate the effect of the unobservable range and the error introduced by arithmetic operations performed on the finite wordlength computer. On the basis of the initial range expressed by several parameterized models, a rootmeansquare cubature Kalman filter is run corresponding to every interval, and the probability is computed according to the Bayesian rule. All the weighting outputs of the filters are summed as the final estimation. Simulation results show the rootmeansquare cubature Kalman filter can obtain better accuracy and robustness although the complexity of the algorithm is a little too high.