Estimating the complex maneuvering of a UGV using IMM-SIF

Abstract

Estimating the position of a unmanned ground vehicle (UGV) that is navigating a complex road is a challenging task. Numerous algorithms have been developed to estimate the maneuvering status of the UGV. In this study, a newly developed filtering technique called the sliding innovation filter (SIF) is combined with multiple model technique to improve the estimation accuracy. The SIF uses the measured states as a discontinuous hyperplane to constrain the estimates to stay close to it. By combining the benefits of both methods, the proposed filter minimizes chatter during position estimation when the UGV is maneuvering. The effectiveness of the proposed method is evaluated on a UGV navigating an S-shaped road, and the results are compared to those obtained using the standard SIF.