Estimation of a Human-Maneuvered Target Incorporating Human Intention

Abstract

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.

Discovery Learning Research in Mathematics Learning: A Bibliometric Review

“…Education is very important and can improve the quality of human resources (Qin et al, 2021;Sohilait, 2021;Widiantari et al, 2022). According to (Sudarsana et al, 2019;Widyanti et al, 2021) Education is a conscious effort to create a learning process that can develop students' self-potential and become human beings with noble character.…”