Hu Yumei, Pan Quan, Deng Bao, Guo Zhen, Li Menghua, Chen Lifeng
Xi'an Aeronautics Computing Technique Research Institute, AVIC, Xi'an 710069, China.
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.
Entropy (Basel). 2023 Aug 18;25(8):1235. doi: 10.3390/e25081235.
The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér-Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér-Rao lower bounds, root-mean-squared errors and the 3σ bound.
变分贝叶斯方法通过迭代计算边际密度的积分来解决非线性估计问题。许多研究人员已经证明，其性能取决于迭代中变分密度计算中的线性近似以及潜在场景的非线性程度。本文采用两种计算变分密度的方法，即自然梯度法和同时扰动随机法，实现了一种基于多普勒测量的用于机动目标跟踪的变分贝叶斯卡尔曼滤波器。后者是从受单跳网络约束的一组传感器收集的。我们针对网络化机动目标跟踪场景提出了一种分布式融合变分贝叶斯卡尔曼滤波器，并给出了所提方法的证据下界和后验克拉美罗下界。在后置克拉美罗下界、均方根误差和3σ界方面，将仿真结果与集中式融合进行了比较。
