A Fuzzy-based Adaptive Unscented Kalman Filter for State Estimation of Three-dimensional Target Tracking

Kumar, Manav; Mondal, Sharifuddin

doi:10.1007/s12555-022-0441-9

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…In terms of academic research, the state estimation problem of nonlinear systems is solved by a variety of filter designs, mainly the Kalman Filter (KF) [ 8 ], Extended Kalman Filter (EKF) [ 9 , 10 ], Unscented Kalman Filter (UKF) [ 11 , 12 ], and Cubature Kalman Filter (CKF) [ 13 , 14 ]. These traditional methods all use the idea of KF to represent the system state and uncertainty with state vectors and covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cubature Information Filtering Method for State Estimation in Bearing-Only Sensor Network

Chen,

Fu,

Zhang

et al. 2024

Entropy

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The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks.

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