Accurate vehicle position is crucial to vehicular Ad-Hoc network (VANET) applications. The wildly used global navigation satellite systems (GNSS) have limited localization accuracy, especially when GNSS signals are bl...
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Accurate vehicle position is crucial to vehicular Ad-Hoc network (VANET) applications. The wildly used global navigation satellite systems (GNSS) have limited localization accuracy, especially when GNSS signals are blocked or contaminated with reflected signals. Cooperative localization based on multi-vehicle information fusion will be the core component in VANET in the near future. This paper strives to enhance the localization accuracy using the fusion of GNSS and relative range information in static scenes. The formulation following the Maximum-Likelihood strategy is a quadratic non-convex programming, which is NP-hard. An iterative localization algorithm is proposed, where an approximate quadratic programming with linear constraints is solved in each iteration. Additional inspection steps and correction step are designed to ensure the convergence and feasibility of the proposed algorithm. The theoretical analysis shows that the limit of the proposed algorithm must be a global optimal solution of the formulated problem and the mean squared error can reach the Cramer-Rao lower bound. In terms of localization accuracy, the proposed method outperforms the existing linearized weighted least-squares method and semidefinite relaxation method, while exhibiting moderate computational complexity.
The article integrates the theoretical study, simulation validation and performance analysis to make a deep research on distributed implementation of iterativelocalization technology in wireless sensor networks. Firs...
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The article integrates the theoretical study, simulation validation and performance analysis to make a deep research on distributed implementation of iterativelocalization technology in wireless sensor networks. Firstly, we utilize Kalman filter method based on square-root cubature to estimate and correct the node's position in real time. We establish a distributed iterative localization algorithm where the nodes that get localized in the current generation serve as references for remaining nodes to localize and the localization process is repeated. In order to improve the deficiency on location accuracy generated from propagating localization errors, and then we formulate the error feedback control method with Taylor series expansion as condition of evaluating whether a node succeed in localization, which is applied to iterativelocalization to establish a Taylor feedback Kalman filter localizationalgorithm. The simulation validation shows that the location accuracy of this kind of algorithm can fully meet the location requirement of the wireless sensor networks. Compared with the traditional method, the results illustrate the performance advantages of the error feedback control method and its contribution to the accuracy of the node position estimation.
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