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北京航空航天大学 [6]

西安交通大学 [5]

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山东大学 [1]

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	A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering 期刊论文 Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2018, 卷号: 211, 页码: 597-608 作者: Tian, D.; Liu, W.; Duan, X.; Rong, H.; Guo, P. 收藏 \| 浏览/下载：10/0 \| 提交时间：2019/12/30 5G mobile communication systems Data fusion Dedicated short range communications Extended Kalman filters Global positioning system Vehicle to vehicle communications Vehicles Wireless networks Doppler frequency shift Extended Kalman filtering Imm-ekf Interacting multiple model filter (IMM) Observation model Positioning enhancements Radio signals Target vehicles Kalman filters
	A DDF-based IMM-TFS Approach for the Accuracy Evaluation Problem of Rapid Transfer Alignment 期刊论文 JOURNAL OF NAVIGATION, 2018, 卷号: 71, 页码: 749-768 作者: Zhou, Dapeng; Guo, Lei 收藏 \| 浏览/下载：9/0 \| 提交时间：2019/12/30 Rapid transfer alignment Accuracy evaluation Divided difference Filter (DDF) Interacting Multiple Model Two-Filter Smoother (IMM-TFS)
	Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy 期刊论文高技术通讯（英文版）, 2017, 卷号: 23, 期号: 2, 页码: 149-155 作者: Hu Zhentao[1]; Zhang Jin[2]; Fu Chunling[3]; Li Xian[4] 收藏 \| 浏览/下载：3/0 \| 提交时间：2019/12/23 maneuvering target tracking interacting multiple model (IMM) central differ-ence Kalman filter ( CDKF) bootstrapping observation
	Distributed estimation for stochastic hybrid systems with event-triggered sensor schedule 期刊论文 IET CONTROL THEORY AND APPLICATIONS, 2017, 卷号: 11, 页码: 173-181 作者: Zhang, Cui; Jia, Yingmin 收藏 \| 浏览/下载：6/0 \| 提交时间：2019/12/30 stochastic systems closed loop systems Monte Carlo methods least mean squares methods sensors manoeuvring target tracking Monte Carlo simulations linear minimum mean square error sensor measurements closed-loop schedule rule event-triggered state estimator IMM approach interacting multiple model event-triggered sensor schedule stochastic hybrid systems
	Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation 期刊论文 CHINESE JOURNAL OF ELECTRONICS, 2016, 卷号: 25, 期号: [db:dc_citation_issue], 页码: 270-276 作者: Zhu Hongyan; Guo Kai; Chen Shuo 收藏 \| 浏览/下载：3/0 \| 提交时间：2019/12/02 Gaussian mixture model (GMM) Estimation fusion Covariance intersection (CI) Interacting multiple model (IMM) Cross-correlation
	Kullback-Leibler divergence for interacting multiple model estimation with random matrices 期刊论文 IET SIGNAL PROCESSING, 2016, 卷号: 10, 页码: 12-18 作者: Li, Wenling; Jia, Yingmin 收藏 \| 浏览/下载：3/0 \| 提交时间：2019/12/30 state estimation filtering theory random processes covariance matrices Markov processes measurement errors statistical distributions inverse problems minimisation method of moments linear systems interacting multiple model estimation random matrix IMM jump Markov linear system unknown measurement noise covariance system state estimation weighted inverse-Wishart distribution weighted Kullback-Leibler divergence minimisation optimisation problem moment matching approac
	Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update 期刊论文高技术通讯（英文版）, 2015, 卷号: 21, 期号: 1, 页码: 39-45 作者: Hu, Zhentao[1]; Fu, Chunling[2]; Cao, Zhiwei[3]; Li, Congcong[4] 收藏 \| 浏览/下载：9/0 \| 提交时间：2019/12/23 maneuvering target tracking nonlinear filtering cubature Kalman filter (CKF) interacting multiple model (IMM)
	An Efficient Algorithm Unifying MHT and Multiple Model Approaches 会议论文 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015-01-01 作者: Liu, Weihua; Zhao, Yongjia; Lei, Xiaoyong 收藏 \| 浏览/下载：4/0 \| 提交时间：2020/01/06 Multiple Hypothesis Tracking (MHT) Interacting Multiple Model (IMM) Multiple Model (MM) Multiple Target Tracking
	Fusion of Gaussian mixture models for possible mismatches of sensor model 期刊论文 INFORMATION FUSION, 2014, 卷号: 20, 期号: [db:dc_citation_issue], 页码: 203-212 作者: Zhu, Hongyan; Chen, Shuo; Han, Chongzhao 收藏 \| 浏览/下载：2/0 \| 提交时间：2019/12/03 Gaussian mixture model (GMM) Estimation fusion Interacting multiple model (IMM)
	Dynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm 期刊论文 IET SIGNAL PROCESSING, 2014, 卷号: 8, 期号: [db:dc_citation_issue], 页码: 202-210 作者: Mao, Yanhui; Han, Chongzhao; Duan, Zhansheng 收藏 \| 浏览/下载：7/0 \| 提交时间：2019/12/03 IMM algorithm dynamic error spectrum dynamic systems interacting multiple model algorithm estimation theory DES metric multiple model algorithm mean square error methods root-mean-square error performance evaluation estimation performance

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