Towards low‐complexity state estimation for rigid bodies based on range difference measurements
The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to esti...
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Published in | Electronics letters Vol. 59; no. 22 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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John Wiley & Sons, Inc
01.11.2023
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Abstract | The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises.
This paper proposes a lightweight rigid body localization algorithm to obtain reliable position and orientation estimators for rigid bodies. Simulations show that the closed‐form algorithm achieves the CRLB performance over a small noise region. |
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AbstractList | The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises. The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises. Abstract The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises. The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the interactions between sensors and landmarks. The prevalent RBL methods employ precise time‐of‐flight measurements (or range measurements) to estimate the state. However, the clock offsets in range measurements in asynchronous networks are unavoidable, leading to performance degradation for state estimators. Therefore, range difference measurements have been adopted to solve the RBL problem. However, existing approaches struggle to achieve desirable performance while maintaining computational efficiency. To address this issue, a new closed‐form state estimator for asynchronous networks is introduced. The proposed algorithm leverages the Taylor‐series expansion technique to enhance accuracy while keeping computational overhead low. Numerical experiments demonstrate that the proposed method achieves state‐of‐the‐art performance with high computational efficiency under small Gaussian noises. This paper proposes a lightweight rigid body localization algorithm to obtain reliable position and orientation estimators for rigid bodies. Simulations show that the closed‐form algorithm achieves the CRLB performance over a small noise region. |
Author | Wang, Zhi Sun, Peng Wang, Yuwei |
Author_xml | – sequence: 1 givenname: Yuwei orcidid: 0000-0001-8099-8445 surname: Wang fullname: Wang, Yuwei organization: Zhejiang University – sequence: 2 givenname: Peng surname: Sun fullname: Sun, Peng organization: Hunan University – sequence: 3 givenname: Zhi surname: Wang fullname: Wang, Zhi email: zjuwangzhi@zju.edu.cn organization: Zhejiang University |
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Cites_doi | 10.1109/TWC.2019.2938761 10.1109/IROS45743.2020.9340924 10.1109/TCOMM.2019.2930517 10.1109/78.301830 10.1109/LCOMM.2015.2486769 10.1109/TVT.2020.3034800 10.1007/s001380050048 10.1109/TSP.2015.2465356 10.1109/TSP.2014.2336621 10.1109/LSP.2019.2957674 10.1109/TWC.2018.2889051 10.1109/TSP.2017.2759098 10.1109/JIOT.2022.3150794 |
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Snippet | The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by utilizing the... Abstract The rigid body localization (RBL) technique is capable of estimating the state of a rigid body, including its translation and orientation, by... |
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SubjectTerms | ad hoc networks Algorithms Clocks & watches Computational efficiency Localization location based services Performance degradation Rigid structures Sensors Series expansion signal processing State estimation Unmanned aerial vehicles |
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Title | Towards low‐complexity state estimation for rigid bodies based on range difference measurements |
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