Chen Zhan, Fu Wenxing, Zhang Ruitao, Fang Yangwang, Xiao Zhun
Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
Entropy (Basel). 2024 Mar 7;26(3):236. doi: 10.3390/e26030236.
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.
基于纯方位传感器的状态估计问题日益重要，而现有的分布式滤波解决方案研究相当有限。因此，本文提出了一种新颖的分布式容积信息滤波（DCIF）方法，以应对纯方位传感器网络中的状态估计挑战。首先，构建了纯方位传感器网络的系统模型，并分析了系统的可观测性。传感器节点成对测量相对角度信息。随后，采用协同一致性理论实现对机动目标的统一状态估计。DCIF方法提高了系统的可观测性，解决了传统非线性滤波算法中精度误差大及发散的问题。基于DCIF一致性收敛的理论证明，进行了四个仿真实验进行比较。这些实验验证了DCIF方法在纯方位传感器网络中的有效性和优越性。
