[1]

潘泉, 程咏梅, 梁彦, 杨峰, 王小旭.多源信息融合理论及应用.北京:清华大学出版社, 2013.

Pan Quan, Cheng Yong-Mei, Liang Yan, Yang Feng, Wang Xiao-Xu. Multisource Information Fusion Theory and Application. Beijing: Tsinghua University Press, 2013.

[2]

Llinas J, Waltz E. Multisensor Data Fusion. Norwood, MA: Artech House Publisher, 1990.

[3]

何友, 修建娟, 关欣.雷达数据处理及应用.第3版.北京:电子工业出版社, 2013.

He You, Xiu Jian-Juan, Guan Xin. Radar Data Processing with Applications (Third Edition). Beijing: Publishing House of Electronics Industry, 2013.

[4]

Hall D L, Llinas J. Handbook of Multisensor Data Fusion. Danvers: CRC Press, 2001.

[5]

周宏仁, 敬忠良, 王培德.机动目标跟踪.北京:国防工业出版社, 1991.

Zhou Hong-Ren, Jing Zhong-Liang, Wang Pei-De. Maneuvering Target Tracking. Beijing: National Defense Industry Press, 1991.

[6]

Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. New York: John Wiley and Sons, 2004.

[7]

王增福, 潘泉, 梁彦, 刘慧霞.天波超视距雷达数据处理算法综述.中国电子科学研究院学报, 2011, 6(5): 477-484

Wang Zeng-Fu, Pan Quan, Liang Yan, Liu Hui-Xia. A review of data processing algorithms for Over-The-Horizon radar. Journal of China Academy of Electronics and Information Technology, 2011, 6(5): 477-484

[8]

Yadav S, Shroff G, Hassan E, Agarwal P. Business data fusion. In: Proceedings of the 18th IEEE Conference on Information Fusion. Washington, DC, USA: IEEE, 2015. 1876-1885

[9]

Chang Y F, Chen C C, Lin S C. An intelligent context-aware communication system for one single autonomic region to realize smart living. Information Fusion, 2015, 21: 57-67

[10]

Liu Y, Chen X, Cheng J, Peng H. A medical image fusion method based on convolutional neural networks. In: Proceedings of the 20th IEEE Conference on Information Fusion. Xi'an, China: IEEE, 2017. 1070-1077

[11]

Bosman H H W J, Iacca G, Tejada A, Wörtche H J, Liotta A. Spatial anomaly detection in sensor networks using neighborhood information. Information Fusion, 2017, 33: 41-56

[12]

Anderson C, Breimyer P, Foster S, Geyer K, Griffith J D, Heier A, et al. A network science approach to open source data fusion and analytics for disaster response. In: Proceedings of the 2015 18th International Conference on Information Fusion. Washington, DC, USA: IEEE, 2015: 207-214

[13]

潘泉, 于昕, 程咏梅, 张洪才.信息融合理论的基本方法与进展.自动化学报, 2003, 29(4): 599-615 http://www.aas.net.cn/CN/abstract/abstract13929.shtml

Pan Quan, Yu Xin, Cheng Yong-Mei, Zhang Hong-Cai. Essential methods and progress of information fusion theory. Acta Automatica Sinica, 2003, 29(4): 599-615 http://www.aas.net.cn/CN/abstract/abstract13929.shtml

[14]

潘泉, 王增福, 梁彦, 杨峰, 刘准钆.信息融合理论的基本方法与进展(Ⅱ).控制理论与应用, 2012, 29(10): 1233-1244 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzllyyy201210001

Pan Quan, Wang Zeng-Fu, Liang Yan, Yang Feng, Liu Zhun-Ga. Basic methods and progress of information fusion (Ⅱ). Control Theory and Applications, 2012, 29(10): 1233-1244 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=kzllyyy201210001

[15]

Li X R, Jilkov V P. Survey of maneuvering target tracking. Part V: multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1255-1321

[16]

Gustafsson F, Hendeby G. Some relations between extended and unscented Kalman filters. IEEE Transactions on Signal Processing, 2012, 60(2): 545-555

[17]

Julier S, Uhlmann J, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482

[18]

Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188

[19]

Frank A, Smyth P, Ihler A. Beyond MAP estimation with the track-oriented multiple hypothesis tracker. IEEE Transactions on Signal Processing, 2014, 62(9): 2413-2423

[20]

Li Q, Sun J P, Sun W. An efficient multiple hypothesis tracker using max product belief propagation. In: Proceedings of the 20th International Conference on Information Fusion. Xi'an, China: IEEE, 2017. 1042-1049

[21]

Li X H, Willett P, Baum M, Li Y A. PMHT approach for underwater bearing-only multisensor—multitarget tracking in clutter. IEEE Journal of Oceanic Engineering, 2016, 41(4): 831-839

[22]

Song T, Kim H, Musicki D. Iterative joint integrated probabilistic data association for multitarget tracking. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(1): 642-653

[23]

Oh J, Russell S, Sastry S. Markov chain Monte Carlo data association for multi-target tracking. IEEE Transactions on Automatic Control, 2009, 54(3): 481-497

[24]

Williams J L. Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 1664-1687

[25]

Mahler R. A survey of PHD filter and CPHD filter implementations. In: Proceedings of Signal Processing, Sensor Fusion, and Target Recognition XVI. Orlando, Florida, United States: SPIE, 2007.

[26]

Vo B T, Vo B N, Hoseinnezhad R, Mahler R P S. Robust multi-Bernoulli filtering. IEEE Journal of Selected Topics in Signal Processing, 2013, 7(3): 399-409

[27]

陈辉, 韩崇昭. CBMeMBer滤波器序贯蒙特卡罗实现新方法的研究.自动化学报, 2016, 42(1): 26-36 http://www.aas.net.cn/CN/abstract/abstract18793.shtml

Chen Hui, Han Chong-Zhao. A new sequential Monte Carlo implementation of cardinality balanced multi-target multi-Bernoulli filter. Acta Automatica Sinica, 2016, 42(1): 26-36 http://www.aas.net.cn/CN/abstract/abstract18793.shtml

[28]

Kamen E W. Multiple target tracking based on symmetric measurement equations. IEEE Transactions on Automatic Control, 1989, 37(3): 371-374 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=e2d444e88774d3b4f8b9e78fee1bc182

[29]

Baum M, Noack B, Hanebeck U D. Kalman filter-based SLAM with unknown data association using symmetric measurement equations. In: Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. New York, USA: IEEE, 2015. 49-53

[30]

Fortmann T E, Bar-Shalom Y, Scheffe M. Multi-target tracking using joint probabilistic data association. In: Proceedings of the 19th IEEE Conference on Decision and Control Including the Symposium on Adaptive Processes. New York, USA: IEEE, 1980. 807-812

[31]

Blackman S S. Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 5-18

[32]

Lan H, Wang X Z, Pan Q, Yang F, Wang Z F, Liang Y. A survey on joint tracking using expectation-maximization based techniques. Information Fusion, 2016, 30: 52-68

[33]

Ruan Y H, Willett P. The turbo PMHT. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(4): 1388-1398

[34]

Ruan Y H, Willett P. Multiple model PMHT and its application to the second benchmark radar tracking problem. IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(4): 1337-1350

[35]

Mahler R P S. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178

[36]

Mahler R. PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543

[37]

Ristic B, Vo B T, Vo B N, Farina A. A tutorial on Bernoulli filters: theory, implementation and applications. IEEE Transactions on Signal Processing, 2013, 61(13): 3406-3430

[38]

Vo B T, Vo B N. Labeled random finite sets and multi-object conjugate priors. IEEE Transactions on Signal Processing, 2013, 61(13): 3460-3475

[39]

Vo B N, Mallick M, Bar-Shalom Y, Coraluppi S, Osborne Ⅲ R, Mahler R, Vo B T. Multitarget Tracking. New York: John Wiley and Sons, Inc., 2015.

[40]

杨峰, 王永齐, 梁彦, 潘泉.基于概率假设密度滤波方法的多目标跟踪技术综述.自动化学报, 2013, 39(11): 1944-1956 http://www.aas.net.cn/CN/abstract/abstract18233.shtml

Yang Feng, Wang Yong-Qi, Liang Yan, Pan Quan. A survey of PHD filter based multi-target tracking. Acta Automatica Sinica, 2013, 39(11): 1944-1956 http://www.aas.net.cn/CN/abstract/abstract18233.shtml

[41]

Lee Y J, Kamen E W. SME filter approach to multiple target tracking with false and missing measurements. In: Proceedings of Conference on Signal and Data Processing of Small Targets. New York, USA: SPIE, 1993. 574-586

[42]

Leven W F, Lanterman A D. Multiple target tracking with symmetric measurement equations revisited: unscented Kalman filters, particle filters, and Taylor series expansions. In: Proceedings of International Society for Optical Engineering. New York, USA: SPIE, 2005. 56-67

[43]

Hanebeck U D, Baum M, Willett P. Symmetrizing measurement equations for association-free multi-target tracking via point set distances. In: Proceedings of Conference of Signal Processing, Sensor/Information Fusion, and Target Recognition. New York, USA: SPIE, 2017. 1-14

[44]

吴潇, 黄树彩, 凌强, 钟宇.基于改进对称量测方程的多目标跟踪.系统工程与电子技术, 2016, 38(1): 21-25 http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs201601004

Wu Xiao, Huang Shu-Cai, Ling Qiang, Zhong Yu. Multiple targets tracking using modified symmetric measurement equations. Systems Engineering and Electronics, 2016, 38(1): 21-25 http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs201601004

[45]

Baum M, Hanebeck U D. The kernel-SME filter for multiple target tracking. Computer Science, 2012. 288-295

[46]

Baum M, Yang S S, Hanebeck U D. The kernel-SME filter with false and missing measurements. In: Proceedings of the 19th IEEE International Conference on Information Fusion. Heidelberg, Germany: IEEE, 2016. 424-428

[47]

Hu X Q, Bao M, Zhang X P, Guan L Y, Hu Y H. Generalized iterated Kalman filter and its performance evaluation. IEEE Transactions on Signal Processing, 2015, 63(12): 3204-3217

[48]

Cappe O, Godsill S J, Moulines E. An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 2007, 95(5): 899-924

[49]

Schön T B, Wills A, Ninness B. System identification of nonlinear state-space models. Automatica, 2011, 47(1): 39-49

[50]

Kantas N, Doucet A, Singh S S, Maciejowski J, Chopin N. On Particle methods for parameter estimation in state-space models. Statistical Science, 2015, 30(3): 328-351

[51]

Dahlin J, Schön T B. Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models. arXiv: 1511.01707v7, 2017.

[52]

Schön T B, Lindsten F, Dahlin J, Waagberg J, Naesseth C A, Svensson A, et al. Sequential Monte Carlo Methods for system identification. IFAC Papers On Line, 2015, 48(28): 775-786

[53]

Daum F, Huang J. Particle flow with non-zero diffusion for nonlinear filters. In: Proceedings of Signal Processing, Sensor Fusion, and Target Recognition. Baltimore, Maryland, United States: SPIE, 2013. 1072-1079

[54]

Bunch P, Godsill S. Approximations of the optimal importance density using Gaussian particle flow importance sampling. Journal of the American Statistical Association, 2016, 111(514): 748-762

[55]

Nurminen H, Piche R, Godsill S. Gaussian flow sigma point filter for nonlinear Gaussian state-space models. In: Proceedings of the 20th IEEE International Conference on Information Fusion. Xi'an, China: IEEE, 2017. 445-452

[56]

Arasaratnam I, Haykin S. Cubature Kalman filters. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269

[57]

Norgaard M, Poulsen N K, Ravn O. New developments in state estimation for nonlinear systems. Automatica, 2000, 36(11): 1627-1638

[58]

Smidl V, Quinn A. Variational Bayesian filtering. IEEE Transactions on Signal Processing, 2008, 56(10): 5020-5030

[59]

Wang X X, Liang Y, Pan Q, Wang Z F. General equivalence between two kinds of noise-correlation filters. Automatica, 2014, 50(12): 3316-3318

[60]

Wang X X, Liang Y, Pan Q, Zhao C H, Yang F. Nonlinear Gaussian smoothers with colored measurement noise. IEEE Transactions on Automatic Control, 2015, 60(3): 870-876

[61]

Geng H, Liang Y, Zhang X J. Linear-minimum-mean-square-error observer for multi-rate sensor fusion with missing measurements. IET Control Theory and Applications, 2014, 8(14): 1375-1383

[62]

Wang X X, Liang Y, Pan Q, Zhao C H. Gaussian filter for nonlinear systems with one-step randomly delayed measurements. Automatica, 2013, 49(4): 976-986

[63]

Geng H, Liang Y, Liu Y R, Alsaadi F E. Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs. Information Fusion, 2018, 39: 139-153

[64]

Geng H, Liang Y, Yang F, Xu L F, Pan Q. The joint optimal filtering and fault detection for multi-rate sensor fusion under unknown inputs. Information Fusion, 2016, 29: 57-67

[65]

Qin Y M, Liang Y, Yang Y B, Wang Z F, Wang F. Adaptive filter of non-linear systems with generalised unknown disturbances. IET Radar, Sonar and Navigation, 2014, 8(4): 307-317

[66]

Yang Y B, Liang Y, Pan Q, Qin Y M, Yang F. Distributed fusion estimation with square-root array implementation for Markovian jump linear systems with random parameter matrices and cross-correlated noises. Information Sciences, 2016, 370-371: 446-462

[67]

Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I, Sastry S S. Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control, 2004, 49(9): 1453-1464

[68]

Sun S L, Xie L H, Xiao W D, Xiao N. Optimal filtering for systems with multiple packet dropouts. IEEE Transactions on Circuits and Systems-Ⅱ: Express Briefs, 2008, 55(7): 695-699

[69]

Wang Z D, Yang F W, Ho D W C, Liu X H. Robust H∞ control for networked systems with random packet losses. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 2007, 37(4): 916-924

[70]

Zhang H, Shi Y, Mehr A S. Robust weighted H∞ filtering for networked systems with intermittent measurements of multiple sensors. International Journal of Adaptive Control and Signal Processing, 2011, 25(4): 313-330

[71]

Lu X, Zhang H S, Wang W, Teo K L. Kalman filtering for multiple time-delay systems. Automatica, 2005, 41(8): 1455-1461

[72]

Dey S, Leong A S, Evans J S. Kalman filtering with faded measurements. Automatica, 2009, 45(10): 2223-2233

[73]

Garcia R, Puig J, Ridao P, Cufi X. Augmented state Kalman filtering for AUV navigation. In: Proceedings of the 2002 Conference on Robotics and Automation. Washington, DC, USA: IEEE, 2002. 4010-4015

[74]

Ghaoui L E, Calafiore G. Robust filtering for discrete-time systems with bounded noise and parametric uncertainty. IEEE Transactions on Automatic Control, 2001, 46(7): 1084-1089

[75]

Li X R, Jikov V P, Ru J. Multiple-model estimation with variable structure-part Ⅵ: expectation-mode augmentation. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3): 853-867

[76]

Bishop C M. Pattern Recognition and Machine Learning. New York, USA: Springer, 2007.

[77]

Sung J, Ghahramani Z, Bang S Y. Latent-space variational Bayes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(12): 2236-2242

[78]

Tzikas D G, Likas A C, Galatsanos N P. The variational approximation for Bayesian inference. IEEE Signal Processing Magazine, 2008, 25(6): 131-146

[79]

Thormann K, Sigges F, Baum M. Learning an object tracker with a random forest and simulated measurements. In: Proceedings of the 20th IEEE Conference on Information Fusion. Xi'an, China: IEEE, 2017. 390-394

[80]

Song K, Kim S H, Tak J, Choi H L, Moon I C. Data-driven ballistic coefficient learning for future state prediction of high-speed vehicles. In: Proceedings of the 19th International Conference on Information Fusion. Heidelberg, Germany: IEEE, 2016. 17-24

[81]

Hexeberg S, Flaten A L, Eriksen B O H, Brekke E F. AIS-based vessel trajectory prediction. In: Proceedings of the 20th International Conference on Information Fusion. Xi'an, China: IEEE, 2017. 1019-1026

[82]

Aprile A, Grossi E, Lops M, Venturino L. Track-before-detect for sea clutter rejection: tests with real data. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(3): 1035-1045

[83]

De Laet T, Bruyninckx H, De Schutter J. Shape-based online multitarget tracking and detection for targets causing multiple measurements: variational Bayesian clustering and lossless data association. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2477-2491

[84]

Willett P, Ruan Y, Streit R. PMHT: problems and some solutions. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(3): 738-754

[85]

Li X R. Optimal bayes joint decision and estimation. In: Proceedings of the 10th IEEE International Conference on Information Fusion. Quebec, Canada: IEEE, 2007. 1-8

[86]

Saul L K, Jordan M I. Exploiting tractable substructures in intractable networks. In: Proceedings of Advances in Neural Information Processing Systems. Cambridge: MIT Press, 1995. 486-492

[87]

Lan H, Liang Y, Pan Q, Yang F, Guan C. An EM algorithm for multipath state estimation in OTHR target tracking. IEEE Transactions on Signal Processing, 2014, 62(11): 2814-2826

[88]

Beal M J. Variational algorithms for approximate Bayesian inference[Ph.D. thesis], University of London, UK, 2003.

[89]

Lan H, Pan Q, Yang F, Sun S, Li L. Variational Bayesian approach for joint multitarget tracking of multiple detection systems. In: Proceedings of the 19th IEEE International Conference on Information Fusion. Heidelberg, Germany: IEEE, 2016.

[90]

Jordan M I, Ghahramani Z, Jaakkola T S, Saul L K. An introduction to variational methods for graphical models. Machine Learning, 1999, 37(2): 183-233

[91]

Hoffman M D, Blei D M, Wang C, Paisley J. Stochastic variational inference. The Journal of Machine Learning Research, 2013, 14(1): 1303-1347 http://d.old.wanfangdata.com.cn/Periodical/jsjkxjsxb-e201602014

[92]

Johnson M J, Willsky A S. Stochastic variational inference for Bayesian time series models. In: Proceedings of the 31st International Conference on Machine Learning. Beijing, China: ICML, 2014. 1854-1862

[93]

Salimans T, Kingma D P, Welling M. Markov chain Monte Carlo and variational inference: bridging the gap. arxiv: 1014.6460v4, 2014.

[94]

Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, California: Morgan Kaufmann, 1988.

[95]

Pearl J. Fusion, propagation, and structuring in belief networks. Artificial Intelligence, 1986, 29(3): 241-288

[96]

Berrou C, Glavieux A, Thitimajshima P. Near Shannon limit error-correcting coding and decoding: turbo-codes. In: Proceedings of International Conference on Communications. New York, USA: IEEE, 1993.

[97]

Forney G D. Codes on graphs: Normal realizations. IEEE Transactions on Information Theory, 2001, 47(2): 520-548

[98]

Kschischang F R, Frey B J, Loeliger H A. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 2001, 47(2): 498-519

[99]

Yedidia J S, Freeman W T, Weiss Y. Generalized belief propagation. In: Proceedings of Annual Conference on Neural Information Processing Systems. New York, USA: MIT Press, 2000. 689-695

[100]

Yedidia J S, Freeman W T, Weiss Y. Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory, 2005, 51(7): 2282-2312

[101]

Mooij J M, Kappen H J. On the properties of the Bethe approximation and loopy belief propagation on binary networks. Journal of Statistical Mechanics Theory and Experiment, 2005, 2005(11): Article No.P11012

[102]

Koller D, Friedman N. Probabilistic Graphical Models: Principle and Technique. Cambridge: MIT Press, 2009.

[103]

Särkkä S, Nummenmaa A. Recursive noise adaptive Kalman filtering by variational Bayesian approximations. IEEE Transactions on Automatic Control, 2009, 54(3): 596-600

[104]

Särkkä S, Hartikainen J. Non-linear noise adaptive Kalman filtering via variational Bayes. In: Proceedings of the 2013 IEEE International Workshop on Machine Learning for Signal Processing. New York, USA: IEEE, 2013. 1-6

[105]

Agamennoni G, Nieto J I, Nebot E M. Approximate inference in state-space models with heavy-tailed noise. IEEE Transactions on Signal Processing, 2012, 60(10): 5024-5037

[106]

Li W L, Sun S H, Jia Y M, Du J P. Robust unscented Kalman filter with adaptation of process and measurement noise covariances. Digital Signal Processing, 2016, 48: 93-103

[107]

Li W L, Jia Y M, Du J P, Zhang J. PHD filter for multi-target tracking with glint noise. Signal Processing, 2014, 94: 48-56

[108]

Ardeshiri T, Özkan E, Orguner U, Gustafsson F. Approximate Bayesian smoothing with unknown process and measurement noise covariances. IEEE Signal Processing Letters, 2015, 22(12): 2450-2454

[109]

Huang Y L, Zhang Y G, Wu Z M, Li N, Chambers J. A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices. IEEE Transactions on Automatic Control, 2018, 63(2): 594-601

[110]

Wang C, Blei D M. Variational inference in nonconjugate models. The Journal of Machine Learning Research, 2013, 14(4): 1005-1031 http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1209.4360

[111]

Piché R, Särkkä S, Hartikainen J. Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution. In: Proceedings of the 2012 International Workshop on Machine Learning for Signal Processing. Santander, Spain: IEEE, 2012: 1-6

[112]

Nurminen H, Ardeshiri T, Piché R, Gustafsson F. Robust inference for state-space models with skewed measurement noise. IEEE Signal Processing Letters, 2015, 22(11): 1898-1902

[113]

Turner R, Bottone S, Stanek C. Online variational approximations to non-exponential family change point models: with application to radar tracking. In: Proceedings of the 26th Annual Conference on Neural Information Processing Systems. New York, USA: MIT Press, 2013. 306-314

[114]

Blei D M, Kucukelbir A, McAuliffe J D. Variational inference: a review for statisticians. Journal of the American Statistical Association, 2017112(518): 859-877

[115]

Lázaro G M, van Vaerenbergh S, Lawrence N D. Overlapping mixtures of Gaussian processes for the data association problem. Pattern Recognition, 2012, 45(4): 1386-1395

[116]

Turner R D, Bottone S, Avasarala B. A complete variational tracker. In: Proceedings of the 2014 Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2014. 496-504

[117]

Williams J L, Lau R A. Data association by loopy belief propagation. In: Proceedings of the 13th International Conference on Information Fusion. Edinburgh, UK: IEEE, 2010. 1-8

[118]

Meyer F, Braca P, Willett P, Hlawatsch F. Tracking an unknown number of targets using multiple sensors: A belief propagation method. In: Proceedings of the 19th IEEE International Conference on Information Fusion. Heidelberg, Germany: IEEE, 2016.

[119]

兰华.基于期望最大法联合估计与辨识的远程预警数据处理[博士学位论文], 西北工业大学, 中国, 2014.

Lan Hua. Joint estimation and identification based on expectation maximization for data processing of distance early warning[Ph.D. thesis], Northwestern Polytechnical University, China, 2014.

[120]

Lan H, Sun S, Wang Z F, Pan Q, Zhang Z S. Joint detection and tracking for multipath targets: a variational Bayesian approach. arXiv: 1610.08616, 2016.

[121]

Ranganath R, Gerrish S, Blei D M. Black box variational inference. arXiv: 1401.0118, 2014.

[122]

Haykin S. Cognitive radar: a way of the future. IEEE Signal Processing Magazine, 2006, 23(1): 30-40

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