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Adaptive Quantized Estimation Fusion Using Strong Tracking Filtering and Variational Bayesian
IEEE Transactions on Systems, Man, and Cybernetics: Systems ( IF 8.7 ) Pub Date : 2020-03-01 , DOI: 10.1109/tsmc.2017.2760900
Quanbo Ge , Zhongliang Wei , Mingxin Liu , Junzhi Yu , Chenglin Wen

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications.

中文翻译:

使用强跟踪滤波和变分贝叶斯的自适应量化估计融合

本文深入研究了自适应量化状态估计融合。为了解决随机量化引起的模型失配问题,在以前的工作中已经提出了一些量化卡尔曼滤波器,例如具有强跟踪滤波的量化卡尔曼滤波器(QKF-STF),变分贝叶斯自适应量化卡尔曼滤波器(VB-AQKF) ),以及一个称为变分贝叶斯自适应 QKF-STF (VB-AQKF-STF) 的基于集中融合帧的复量化滤波器。本文在单传感器系统工作的基础上,设计了一个分布式复量化滤波器。提出了一种基于多方法融合方案(QKF-MMF)的新型量化卡尔曼滤波器。类似于 VB-AQKF-STF,QKF-MMF还可以实现分布式融合框架下状态和量化误差协方差的联合估计。此外,它通过使用集中式和分布式融合框架将单传感器结果扩展到多传感器跟踪系统。提出了两个多传感器量化融合估计器,用于融合中心具有主副处理器的并行结构。加权融合和嵌入式集成方式被深入应用于设计多传感器量化融合方法。所提出的工作可以完善量化估计算法,并为实际工程应用提供不同的选择。提出了两个多传感器量化融合估计器,用于融合中心具有主副处理器的并行结构。加权融合和嵌入式集成方式被深入应用于设计多传感器量化融合方法。所提出的工作可以完善量化估计算法,并为实际工程应用提供不同的选择。提出了两个多传感器量化融合估计器,用于融合中心具有主副处理器的并行结构。加权融合和嵌入式集成方式被深入应用于设计多传感器量化融合方法。所提出的工作可以完善量化估计算法,并为实际工程应用提供不同的选择。
更新日期:2020-03-01
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