Improved Adaptive Algorithm for Ship Trajectory Estimation

Abstract

In view of the combination of sage-husa and strong track filtering (STF) algorithm still existing negative definite noise variance, failing to estimate process and measurement noise simultaneously and STF depending on the measurements excessively, proposed a novel approach to solve the problems stated above. In a newly scalar sequence processing way, discussed and modified the convergence criterion. When in its convergence, only updated measurements noise variance under the control of a improved forgetting factor; when in its divergence, updated priori estimate error covariance and measurements noise variance simultaneously to make innovation sequence orthogonal and have same order of magnitude. The experiments results illustrated that compared with the conventional one, the proposed method could hold the noise variance positive and came to convergence more quickly with higher accuracy. Although the noise variance increased sharply when in the state of divergence, it met our design objective.