融合深度学习的贝叶斯滤波综述

张文安; 林安迪; 杨旭升; 俞立; 杨小牛

doi:10.16383/j.aas.c230457

融合深度学习的贝叶斯滤波综述

doi: 10.16383/j.aas.c230457

张文安^{1, 2,},
林安迪^{1, 2,},
杨旭升^{1, 2,},
俞立^{1, 2,},
杨小牛^{1, 3,}

1.
浙江工业大学信息工程学院杭州 310023
2.
浙江省嵌入式系统联合重点实验室杭州 310023
3.
电磁空间安全全国重点实验室嘉兴 314033

基金项目: 国家自然科学基金(62173305), 浙江省“尖兵”、“领雁”研发攻关计划(2022C03114), 浙江省科技计划项目(2023C04032)资助

详细信息

作者简介:
张文安：浙江工业大学信息工程学院教授. 主要研究方向为多源信息融合估计和网络化系统. E-mail: wazhang@zjut.edu.cn

林安迪：浙江工业大学信息工程学院博士研究生. 主要研究方向为多源信息融合估计. E-mail: 201706061126@zjut.edu.cn

杨旭升：浙江工业大学信息工程学院副教授. 主要研究方向为多源信息融合估计和目标定位. 本文通信作者. E-mail: xsyang@zjut.edu.cn

俞立：浙江工业大学信息工程学院教授. 主要研究方向为鲁棒控制, 网络化系统感知与控制. E-mail: lyu@zjut.edu.cn

杨小牛：中国工程院院士, 电磁空间安全全国重点实验室首席科学家. 主要研究方向为软件无线电和智能信号处理. E-mail: yxn2117@126.com

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出版历程
- 收稿日期: 2023-07-25
- 录用日期: 2023-12-21
- 网络出版日期: 2024-04-11
- 刊出日期: 2024-08-22

A Survey on Bayesian Filtering With Deep Learning

ZHANG Wen-An^{1, 2
,},
LIN An-Di^{1, 2
,},
YANG Xu-Sheng^{1, 2
,},
YU Li^{1, 2
,},
YANG Xiao-Niu^{1, 3
,}

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023
2.
Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023
3.
National Key Laboratory of Electromagnetic Space Security, Jiaxing 314033

Funds: Supported by National Natural Science Foundation of China (62173305), “Pioneer”, “Leading Goose” Research and Development Program of Zhejiang Province (2022C03114), and Development Program of Zhejiang Province (2023C04032)

More Information

Author Bio:
ZHANG Wen-An　Professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers multi-sensor information fusion estimation and networked systems

LIN An-Di　Ph.D. candidate at the College of Information Engineering, Zhejiang University of Technology. His main research interest is multi-sensor information fusion estimation

YANG Xu-Sheng　Associate professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers multi-sensor information fusion estimation and target positioning. Corresponding author of this paper

YU Li　Professor at the College of Information Engineering, Zhejiang University of Technology. His research interest covers robust control and perception and control of networked systems

YANG Xiao-Niu　Academician of Chinese Academy of Engineering, chief scientist at the National Key Laboratory of Electromagnetic Space Security. His research interest covers software radio and intelligent signal processing

摘要

摘要: 当前动态系统呈现大型化、复杂化的趋势, 基于贝叶斯滤波的动态系统状态估计遇到一系列新的挑战. 随着深度学习在特征提取与模式识别等方面的优势与潜力不断显现, 深度学习与传统贝叶斯滤波相结合的研究也随之兴起. 为此, 梳理了不同领域融合深度学习的贝叶斯滤波方法的应用案例, 从中剖析不同类型动态系统下贝叶斯滤波存在的局限性和共性难题. 在此基础上, 总结了当前贝叶斯滤波存在的几类不确定性问题, 以深度学习的视角将这些问题归纳为特征提取和参数辨识两大基本问题, 进而介绍深度学习为贝叶斯滤波所提供的解决方案. 其次, 归纳整理了两类深度学习与贝叶斯滤波结合的具体方法, 着重介绍了深度卡尔曼滤波和融合深度学习的自适应卡尔曼滤波. 最后, 综合考虑深度学习方法和贝叶斯滤波方法的优势, 讨论了融合深度学习的贝叶斯滤波方法的开放问题和未来研究方向.
- 深度学习 /
- 贝叶斯滤波 /
- 卡尔曼滤波 /
- 状态估计 /
- 状态空间模型
Abstract: As dynamic systems continue to exhibit a trend towards increased scale and complexity, the Bayesian filtering based state estimation for dynamic systems faces a series of new challenges. With the increasing prominence and new potential of deep learning in areas such as feature extraction and pattern recognition, research on combination of deep learning and classical Bayesian filtering is emerging. In this paper, we present a systematic review of application cases of Bayesian filtering methods that integrate deep learning in different domains, aiming to analyze the limitations and common challenges of Bayesian filtering in various types of dynamic systems. In view of this, we summarize several categories of uncertainty problems in the existing Bayesian filtering. From the perspective of deep learning, these problems are classified into two fundamental problems: Feature extraction and parameter identification. Furthermore, we introduce the solutions provided by deep learning for Bayesian filtering. Additionally, we categorize and organize two specific approaches that combine Bayesian filtering with deep learning, that is, deep Kalman filtering and adaptive Kalman filtering with deep learning. Finally, by considering the advantages of both deep learning and Bayesian filtering methods, we discuss open questions and future research directions for Bayesian filtering with deep learning.
- Deep learning /
- Bayesian filtering /
- Kalman filtering /
- state estimation /
- state-space model

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图 1 本文结构及主要内容

Fig. 1 The structure and main contents in this paper

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图 2 不确定性问题

Fig. 2 The uncertainty issues

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图 3 使用神经网络学习系统状态转移过程

Fig. 3 Learning state transition with neural networks

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图 4 Backprop KF的动态结构图

Fig. 4 Dynamic structure diagram of Backprop KF

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图 5 基于深度状态空间模型的卡尔曼滤波框架

Fig. 5 Framework of Kalman filtering based on deep state-space model

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图 6 KalmanNet中的卡尔曼增益计算流程

Fig. 6 The Kalman gain computation process in KalmanNet

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图 7 使用深度神经网络估计噪声统计特性

Fig. 7 Estimating noise statistical characteristics with deep neural networks

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图 8 基于深度学习的多模型自适应卡尔曼滤波的两类思路

Fig. 8 Two types of ideas for multi model adaptive Kalman filtering based on deep learning

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