Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 70, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3127634
Keywords
Estimation; Oceans; Mathematical models; Navigation; Global Positioning System; Convergence; Vehicle dynamics; Cooperative estimation; flow field; motion-integration error; multi-autonomous underwater vehicle (AUV); parametric model
Funding
- Zhejiang Provincial Natural Science Foundation of China [LZ19F030002]
- National Natural Science Foundation of China [61873235]
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1909206, U1809212, U1709203]
- Key Research and Development Program of Zhejiang Province [2019C03109]
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This article investigates the cooperative estimation problem of recovering the parametric flow field using sensor measurements from an AUV team. It establishes a parametric flow model and formulates a set of nonlinear equations to present the deterministic and continuous relationship between errors and the flow model parameters. The proposed iterative algorithm estimates model parameters by solving an inverse problem for these nonlinear equations and simulations illustrate its effectiveness.
This article investigates the cooperative estimation problem to recover the parametric flow field through sensor measurements from an autonomous underwater vehicle (AUV) team. We establish the parametric flow model that incorporates the concept of incompressibility to provide a physical property. Then, considering the influence of unknown flow field on AUVs' trajectories while submerged, we define: 1) the deviation between actual and predicted relative positions between each vehicle and its neighbors as an relative motion-integration error, which is available using local measurement and 2) the deviation between the actual and predicted position of each vehicle as an absolute motion-integration error, which is available from global positioning system (GPS) when the AUVs are on the sea surface. Based on relative and absolute motion-integration errors, we formulate a set of nonlinear equations to present the deterministic and continuous relationship between the above errors and the parametric flow model. To reconstruct the flow field, an iterative algorithm is proposed to estimate the model parameters by solving an inverse problem for these nonlinear equations. Moreover, a detailed convergence analysis of the proposed algorithm is given. Finally, simulations are conducted to illustrate the effectiveness of the proposed algorithm in a simulated and real dataset of the flow field.
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