1. <button id="qm3rj"><thead id="qm3rj"></thead></button>
      <samp id="qm3rj"></samp>
      <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source>

      <video id="qm3rj"><code id="qm3rj"></code></video>

        1. <tt id="qm3rj"><track id="qm3rj"></track></tt>
            1. 2.765

              2022影響因子

              (CJCR)

              • 中文核心
              • EI
              • 中國科技核心
              • Scopus
              • CSCD
              • 英國科學文摘

              留言板

              尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

              姓名
              郵箱
              手機號碼
              標題
              留言內容
              驗證碼

              基于自然梯度的非線性變分貝葉斯濾波算法

              胡玉梅 潘泉 鄧豹 郭振 陳立峰

              胡玉梅, 潘泉, 鄧豹, 郭振, 陳立峰. 基于自然梯度的非線性變分貝葉斯濾波算法. 自動化學報, 2024, 50(4): 1?15 doi: 10.16383/j.aas.c230359
              引用本文: 胡玉梅, 潘泉, 鄧豹, 郭振, 陳立峰. 基于自然梯度的非線性變分貝葉斯濾波算法. 自動化學報, 2024, 50(4): 1?15 doi: 10.16383/j.aas.c230359
              Hu Yu-Mei, Pan Quan, Deng Bao, Guo Zhen, Chen Li-Feng. A novel nonlinear variational bayesian filtering algorithm using natural gradient. Acta Automatica Sinica, 2024, 50(4): 1?15 doi: 10.16383/j.aas.c230359
              Citation: Hu Yu-Mei, Pan Quan, Deng Bao, Guo Zhen, Chen Li-Feng. A novel nonlinear variational bayesian filtering algorithm using natural gradient. Acta Automatica Sinica, 2024, 50(4): 1?15 doi: 10.16383/j.aas.c230359

              基于自然梯度的非線性變分貝葉斯濾波算法

              doi: 10.16383/j.aas.c230359
              基金項目: 國家自然科學基金(61790552, 61976080)資助, 西北工業大學博士論文創新基金(CX201915)資助
              詳細信息
                作者簡介:

                胡玉梅:航空工業西安航空計算技術研究所工程師. 主要研究方向為多源信息融合, 航空電子系統. E-mail: hym_henu@163.com

                潘泉:西北工業大學自動化學院教授, 信息融合技術教育部重點實驗室主任. 主要研究方向為信息融合理論, 目標跟蹤與識別技術, 無人機探測導航與安全控制. 本文通信作者. E-mail: quanpan@nwpu.edu.cn

                鄧豹:航空工業西安航空計算技術研究所研究員. 主要研究方向為航空電子系統, 分布式并行處理. E-mail: dengbao15@sina.com

                郭振:湖北航天技術研究院總體設計所工程師. 主要研究方向為多源信息融, 目標跟蹤. E-mail: guozhennpu@126.com

                陳立峰:湖北三江航天險峰電子信息有限公司研究員. 主要研究方向為目標探, 雷達信號處理. E-mail: chenlf22@mails.tsinghua.edu.cn

              A Novel Nonlinear Variational Bayesian Filtering Algorithm Using Natural Gradient

              Funds: Supported by National Natural Science Foundation of China (61790552, 61976080) and Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (CX201915)
              More Information
                Author Bio:

                HU Yu-Mei Engineer at Xi'an Aeronautics Computing Technique Research Institute, AVIC. Her research interest covers information fusion and avionics systems

                PAN Quan Professor at the School of Automation, Northwestern Polytechnical University. He is also the Director of the Key Laboratory of Information Fusion Technology, Ministry of Education. His research interest covers information fusion theory, target tracking and recognition technology, and UAV detection navigation and safely control. Corresponding author of this paper

                DENG Bao Professor at Xi'an Aeronautics Computing Technique Research Institute, AVIC. His research interest covers avionics systems and distributed parallel processing

                GUO Zhen Engineer at System Design Institute, Hubei Aerospace Technology Academy. His research interest covers multi-source information fusion, target tracking

                CHEN Li-Feng Professor at Hubei Sanjiang Aerospace Xianfeng Electronic Information Co., Ltd. His research interest covers target detection and radar signal processing

              • 摘要: 在統計流形空間中, 從信息幾何角度考慮非線性狀態后驗分布近似的實質是后驗分布與相應參數化變分分布之間的Kullback-Leibler散度最小化問題, 同時也可以轉化為變分置信下界的最大化問題. 為了提升非線性系統狀態估計的精度, 在高斯系統假設條件下結合變分貝葉斯推斷和Fisher信息矩陣推導出置信下界的自然梯度, 并通過分析其信息幾何意義, 闡述在統計流形空間中置信下界沿其方向不斷迭代增大, 實現變分分布與后驗分布的 “緊密” 近似; 在此基礎上, 以狀態估計及其誤差協方差作為變分超參數, 結合最優估計理論給出一種基于自然梯度的非線性變分貝葉斯濾波算法; 最后, 通過天基光學傳感器量測條件下近地軌道衛星跟蹤定軌仿真實驗驗證: 與對比算法相比, 所提算法具有更高的精度.
              • 圖  1  變量分布近似過程中的KL散度示意圖

                Fig.  1  The KL divergence of the distribution approximation of a variable

                圖  2  非線性動態系統狀態轉移和量測的示意圖

                Fig.  2  The state transition and measurement in a nonlinear dynamic system

                圖  3  單變量高斯分布的歐氏距離示意圖

                Fig.  3  The Euclidean distance for univariate Gaussian distributions

                圖  4  多變量高斯分布的歐氏距離示意圖

                Fig.  4  The Euclidean distance for multivariate Gaussian distributions

                圖  5  非線性狀態估計在不同空間中的含義示意圖

                Fig.  5  The meaning of nonlinear state estimation in different spaces

                圖  6  O沿切向量方向向點P處移動的示意圖

                Fig.  6  Point O moves in the tangential direction towards P

                圖  7  變分迭代過程中置信下界自然梯度的示意圖

                Fig.  7  The natural gradient of ELBO in variation1al Bayesian iteration

                圖  8  天基量測條件下LEO跟蹤定軌仿真場景

                Fig.  8  Scenario of LEO orbit determination and tracking with space-based measurement

                圖  9  x軸位置估計RMSE的對比

                Fig.  9  RMSE of position estimation in x axis

                圖  10  y軸位置估計RMSE的對比

                Fig.  10  RMSE of position estimation in y axis

                圖  11  z軸位置估計RMSE的對比

                Fig.  11  RMSE of position estimation in z axis

                圖  12  x軸速度估計RMSE的對比

                Fig.  12  RMSE of velocity estimation in x axis

                圖  13  y軸速度估計RMSE的對比

                Fig.  13  RMSE of velocity estimation in y axis

                圖  14  z軸速度估計RMSE的對比

                Fig.  14  RMSE of velocity estimation in z axis

                表  1  目標的軌道根數

                Table  1  The orbital elements of target

                半長軸 (km) 離心率 傾角 (deg) 近地點角 (deg) 升交點赤經 (deg)
                7500 0.1 15 30 12
                下載: 導出CSV

                表  2  算法平均運行時間$( \times 10^{-4}\;{\rm{s}}) $的對比

                Table  2  The comparison of the average run time $( \times 10^{-4}\;{\rm{s}}) $ of algorithms

                算法 EKF UKF IEKF VBKF-NG
                時間 0.2580 0.9542 1.0989 1.1205
                下載: 導出CSV
                1. <button id="qm3rj"><thead id="qm3rj"></thead></button>
                  <samp id="qm3rj"></samp>
                  <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source>

                  <video id="qm3rj"><code id="qm3rj"></code></video>

                    1. <tt id="qm3rj"><track id="qm3rj"></track></tt>
                        亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页
                      1. [1] 潘泉, 胡玉梅, 蘭華, 孫帥, 王增福, 楊峰. 信息融合理論研究進展: 基于變分貝葉斯的聯合優化. 自動化學報, 2019, 45(7): 1207-1233

                        Pan Quan, Hu Yu-Mei, Lan Hua, Sun Shuai, Wang Zeng-Fu, Yang Feng. Information fusion progress: Joint optimization based on variational Bayesian theory. Acta Automatica Sinica, 2019, 45(7): 1207-1223
                        [2] Ito K, Xiong K Q. Gaussian filters for nonlinear filtering problems. IEEE Transactions on Automatic Control, 2000, 45(5): 910-927 doi: 10.1109/9.855552
                        [3] Hu Y M, Wang X Z, Pan Q, Hu Z T, Moran B. Variational Bayesian Kalman filter using natural gradient. Chinese Journal of Aeronautics, 2022, 35(5): 1-10 doi: 10.1016/j.cja.2021.08.033
                        [4] Gu D B. A game theory approach to target tracking in sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 41(1): 2-13 doi: 10.1109/TSMCB.2010.2040733
                        [5] 潘泉, 胡玉梅, 馬季容. 基于變分貝葉斯聯合優化的情報監視與偵察. 指揮信息系統與技術, 2020, 11(2): 1-8

                        Pan Quan, Hu Yu-Mei, Ma Ji-Rong. Intelligence, surveillance and reconnaissance based on variational Bayesian joint optimization. Command Information System and Technology, 2020, 11(2): 1-8
                        [6] Hu X Y, Yang L Q, Xiong W. A novel wireless sensor network frame for urban transportation. IEEE Internet of Things Journal, 2015, 2(6): 586-595 doi: 10.1109/JIOT.2015.2475639
                        [7] Spantini A, Baptista R, Marzouk Y. Coupling techniques for nonlinear ensemble filtering. SIAM Review, 2022, 64(4): 921-953 doi: 10.1137/20M1312204
                        [8] Silva B, Fisher R M, Kumar A, Hancke G P. Experimental link quality characterization of wireless sensor networks for underground monitoring. IEEE Transactions on Industrial Informatics, 2015, 11(5): 1099-1110 doi: 10.1109/TII.2015.2471263
                        [9] Vu T, Rahmani A. Distributed consensus-based Kalman filter estimation and control of formation flying spacecraft: Simulation and validation. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference. Kissimmee, USA: AIAA, 2015. 7?12
                        [10] Wang B H, Chen W S, Zhang B, Shi P, Zhang H Y. A nonlinear observer-based approach to robust cooperative tracking for heterogeneous spacecraft attitude control and formation applications. IEEE Transactions on Automatic Control, 2023, 68(1): 400-407 doi: 10.1109/TAC.2022.3143082
                        [11] Tronarp F, García-Fernández á F, S?rkk? S. Iterative filtering and smoothing in nonlinear and non-Gaussian systems using conditional moments. IEEE Signal Processing Letters, 2018, 25(3): 408-412 doi: 10.1109/LSP.2018.2794767
                        [12] Humpherys J, West J. Kalman filtering with newton’s method [Lecture Notes]. IEEE Control Systems Magazine, 2010, 30(6): 101-106 doi: 10.1109/MCS.2010.938485
                        [13] Alessandri A, Gaggero M. Moving-horizon estimation for discrete-time linear and nonlinear systems using the gradient and newton methods. In: Proceedings of the IEEE 55th Conference on Decision and Control. Las Vegas, USA: IEEE, 2016. 2906?2911
                        [14] Akyildiz ? D, Chouzenoux é, Elvira V, Míguez J. A probabilistic incremental proximal gradient method. IEEE Signal Processing Letters, 2019, 26(8): 1257-1261 doi: 10.1109/LSP.2019.2926926
                        [15] Gultekin S, Paisley J. Nonlinear Kalman filtering with divergence minimization. IEEE Transactions on Signal Processing, 2017, 65(23): 6319-6331 doi: 10.1109/TSP.2017.2752729
                        [16] Hoffman M D, Blei D M, Wang C, Paisley J. Stochastic variational inference. The Journal of Machine Learning Research, 2013, 14(1): 1303-1347
                        [17] Salimans T, Kingma D P, Welling M. Markov chain Monte Carlo and variational inference: Bridging the gap. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lille, France: JMLR.org, 2015. 1218?1226
                        [18] Acerbi L. Variational Bayesian Monte Carlo. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal, Canada: Curran Associates Inc., 2018. 8223?8233
                        [19] Acerbi L. Variational Bayesian Monte Carlo with noisy likelihoods. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver, Canada: Curran Associates Inc., 2020. Article No. 688
                        [20] Wang P Y, Blunsom P. Collapsed variational Bayesian inference for hidden Markov models. In: Proceedings of the 16th International Conference on Artificial Intelligence and Statistics. Scottsdale, USA: AISTATS, 2013. 599?607
                        [21] Amari S I. Natural gradient works efficiently in learning. Neural Computation, 1998, 10(2): 251-276 doi: 10.1162/089976698300017746
                        [22] 李宇波. 基于信息幾何理論的濾波方法研究 [博士學位論文], 國防科技大學, 中國, 2017

                        Li Yu-Bo. Study of Filtering Methods via Information Geometry [Ph.D. dissertation], National University of Defense Technology, China, 2017
                        [23] Zhang G D, Sun S Y, Duvenaud D, Grosse R B. Noisy natural gradient as variational inference. In: Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden: ICML, 2018. 5847?5856
                        [24] Ollivier Y. Online natural gradient as a Kalman filter. Electronic Journal of Statistics, 2018, 12(2): 2930-2961
                        [25] Cheng Y Q, Wang X Z, Morelande M, Moran B. Information geometry of target tracking sensor networks. Information Fusion, 2013, 14(3): 311-326 doi: 10.1016/j.inffus.2012.02.005
                        [26] Schmitt L, Fichter W. Globally valid posterior Cramér-Rao bound for three-dimensional bearings-only filtering. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(4): 2036-2044 doi: 10.1109/TAES.2018.2881352
                        [27] 胡玉梅, 潘泉, 胡振濤, 郭振. 基于自然梯度的噪聲自適應變分貝葉斯濾波算法. 自動化學報, 2023, 49(10): 2094-2108

                        Hu Yu-Mei, Pan Quan, Hu Zhen-Tao, Guo Zhen. A novel noise adaptive variational Bayesian filter using natural gradient. Acta Automatica Sinica, 2023, 49(10): 2094-2108
                        [28] Duan T, Avati A, Ding D Y, Thai K K, Basu S, Ng A, Schuler A. NGBoost: Natural gradient boosting for probabilistic prediction. In: Proceedings of the 37th International Conference on Machine Learning. JMLR.org. Vienna, Austria, 2020. Article No. 252
                        [29] Tseng P. An analysis of the EM algorithm and entropy-like proximal point methods. Mathematics of Operations Research, 2004, 29(1): 27-44 doi: 10.1287/moor.1030.0073
                        [30] Khan M E, Baqué P, Fleuret F, Fua P. Kullback-leibler proximal variational inference. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2015. 3402?3410
                        [31] Smidl V, Quinn A. Variational Bayesian filtering. IEEE Transactions on Signal Processing, 2008, 56(10): 5020-5030 doi: 10.1109/TSP.2008.928969
                        [32] Beal M J, Ghahramani Z. The Variational Kalman Smoother, Technical Report GCNU TR, 2001, 3, Computational Neuroscience Unit, University College London, UK, 2001.
                        [33] Hu Y M, Wang X Z, Lan H, Wang Z F, Moran B, Pan Q. An iterative nonlinear filter using variational Bayesian optimization. Sensors, 2018, 18(12): Article No. 4222
                        [34] Ble D M, Kucukelbir A, McAuliffe J D. Variational inference: A review for statisticians. Journal of the American Statistical Association, 2017, 112(518): 859-877 doi: 10.1080/01621459.2017.1285773
                        [35] Sain R, Mittal V, Gupta V. A comprehensive review on recent advances in variational Bayesian inference. In: Proceedings of the International Conference on Advances in Computer Engineering and Applications. Ghaziabad, India: IEEE, 2015. 488?492
                        [36] Beal M J. Variational Algorithms for Approximate Bayesian Inference [Ph.D. dissertation], Cambridge University, UK, 2003
                        [37] Amari S I. Information Geometry and its Applications. Tokyo: Springer, 2016.
                        [38] Amari S, Douglas S C. Why natural gradient?. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing. Seattle, USA: IEEE, 1998. 1213?1216
                        [39] Rezende D J, Mohamed S, Wierstra D. Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the 31st International Conference on International Conference on Machine Learning. Beijing, China: JMLR.org, 2014. II-1278?II-1286
                        [40] Lan H, Liang Y, Zhang W, Yang F, Pan Q. Iterated minimum upper bound filter for tracking orbit maneuvering targets. In: Proceedings of the 16th International Conference on Information Fusion. Istanbul, Turkey: IEEE, 2013. 1051?1057
                      2. 加載中
                      3. 計量
                        • 文章訪問數:  105
                        • HTML全文瀏覽量:  39
                        • 被引次數: 0
                        出版歷程
                        • 收稿日期:  2023-06-12
                        • 錄用日期:  2023-11-20
                        • 網絡出版日期:  2024-02-19

                        目錄

                          /

                          返回文章
                          返回