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- W4312978701 abstract "Given a network of N static nodes in D-dimensional space and the pairwise distances between them, the challenge of estimating the coordinates of the nodes is a well-studied problem. However, for numerous application domains, the nodes are mobile and the estimation of relative kinematics (e.g., position, velocity and acceleration) is a challenge, which has received limited attention in literature. In this paper, we introduce a time-varying Grammian-based data model for estimating the relative kinematics of mobile nodes with polynomial trajectories, given the time-varying pairwise distance measurements between the nodes. Furthermore, we consider a scenario where the nodes have on-board accelerometers, and extend the proposed data model to include these accelerometer measurements. We propose closed-form solutions to estimate the relative kinematics, based on the proposed data models. We conduct simulations to showcase the performance of the proposed estimators, which show improvement against state-of-the-art methods." @default.
- W4312978701 created "2023-01-05" @default.
- W4312978701 creator A5026120456 @default.
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- W4312978701 date "2022-08-29" @default.
- W4312978701 modified "2023-10-16" @default.
- W4312978701 title "Relative Kinematics Estimation Using Accelerometer Measurements" @default.
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- W4312978701 doi "https://doi.org/10.23919/eusipco55093.2022.9909750" @default.
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