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| 1 | Modified unscented particle filter for nonlinear Bayesian tracking显示文摘A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks (such as, particle impoverishment and numerical sensitivity in calculating the prior) of the conventional unscented particle filter (UPF) confronted in practice. Specifically, a difierent derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution. Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one. | Zhan Ronghui Xin Qin Wan Jianwei | 2008 | Journal of Systems Engineering and Electronics2008，19，1： | 14 |
| 2 | Joint tracking and classification of extended targets with complex shapes显示文摘This paper addresses the problem of joint tracking and classification(JTC) of a single extended target with a complex shape. To describe this complex shape, the spatial extent state is first modeled by star-convex shape via a random hypersurface model(RHM), and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation, while the Euclidean distance metric of the normalized Fourier descriptors is applied to obtain the analytical solution of the updated class probability. Consequently, the resulting method is called the 'JTC-RHM method.' Besides, the proposed JTC-RHM is integrated into a Bernoulli filter framework to solve the JTC of a single extended target in the presence of detection uncertainty and clutter, resulting in a JTC-RHM-Ber filter. Specifically, the recursive expressions of this filter are derived. Simulations indicate that:(1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly, compared with the JTC method based on the random matrix model,(2) the proposed method performs better in target state estimation than the star-convex RHM based extended target tracking method,(3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation, and can achieve target classification correctly. | Liping WANG Ronghui ZHAN Yuan HUANG Jun ZHANG Zhaowen ZHUANG | 2021 | Frontiers of Information Technology & Electronic Engineering2021，22，6： | 2 |
| 3 | Iterated unscented Kalman filter for passive target tracking显示文摘 | ZHAN Ronghui WAN Jianwei | 2007 | IEEE Transactions on Aerosp-ace and Electronic Systems2007，43，3： | 1 |
| 4 | Iterated unscented Kalman filter for passive target tracking显示文摘 | ZHAN Ronghui WAN Jianwei | 2007 | IEEE Transactions on Aerosp-ace and Electronic Systems2007，43，3： | 1 |
| 5 | Neural network-Aided adaptive Unscented Kalman Filter for nonlinear State Estimation 显示文摘 | Ronghui Zhan Jianwei Wan | 2006 | IEEE Signal Processing Letters (S 1070-9908)2006，13，7： | 1 |
| 6 | Iterated unscented Kalman filter for passive target tracking 显示文摘 | ZHAN Ronghui WAN Jianwei | 2007 | IEEE Transactions on Aerospace and Electronic Systems2007，43，3： | 1 |
| 7 | Iterated unscentedKalman filter for passive target tracking 显示文摘 | ZHAN Ronghui WAN Jianwei | 2007 | IEEE Tranon Aerospace and Electronic Systems2007，43，3： | 1 |
| 8 | Iterated unscented Kalman filter for passive target tracking 显示文摘 | ZHAN Ronghui WAN Janwe | 2007 | IEEE Transactions on Aerospace and Electronic Systems2007，43，3： | 1 |
| 9 | Iterated unscented Kalman filter for passive target tracking 显示文摘 | Zhan Ronghui Wan Jianwei | 2007 | IEEE Transactions on Aerospace and Electronic Systems2007，3，3： | 1 |
| 10 | Multi-view radar target recognition based on multitask compressive sensing显示文摘 | LIU Shengqi ZHAN Ronghui ZHAI Qinglin | 2015 | Journal of Electromagnetic Waves and Applications2015，29，14： | 1 |
| 11 | Neural Network-aided Adaptive Unscented Kalman Filter for Nonlinear State Estimation显示文摘 | Zhan Ronghui Wan Jianwei | 2006 | IEEE Signal Processing Letters2006，13，7： | 1 |
| 12 | Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation显示文摘 | Zhan Ronghui Wan Jianwei | 2006 | IEEE Signal Processing Letters2006，13，7： | 1 |
| 13 | Radar automatic target recognition based on sequential vanishing component analysis显示文摘 | LIU Shengqi ZHAN Ronghui ZHANG Jun | 2014 | Progress In Electromagnetics Research2014，145，： | 1 |
| 14 | Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation显示文摘 | Zhan Ronghui Wan Jianwei | 2006 | IEEE Signal Processing Letters2006，13，7： | 1 |
| 15 | Iterated Unscented Kalman Filter for Passive Target Tracking显示文摘 | Zhan Ronghui Wan Jianwei | 2007 | IEEE Transactions on Aerospace and Eletronic Systems2007，43，3： | 1 |
| 16 | Neural network-aided adaptive unscented Kalman filter for nonlinear state estimation显示文摘 | ZHAN Ronghui WAN Jianwei | 2006 | IEEE Signal Processing Letters2006，13，7： | 1 |
| 17 | Neural network-aided adaptive unscented kalman filter for nonlinear state estimation显示文摘 | Zhan Ronghui Wan Jianwei | | 0，，07： | 1 |
| 18 | POSTERIOR CRAMR-RAO BOUNDS ANALYSIS FOR PASSIVE TARGET TRACKING显示文摘For the problem of deterministic parameter estimate, the theoretical lower bound of esti- mate error is the Cramér-Rao bound; while for random parameter, the lower bound of estimate error is generally termed by Posterior Cramér-Rao Bound (PCRB). Under the background of passive tracking where the target's state can be seen as a time-varying random parameter, PCRB of the state estimate error is analyzed in this paper, and the relation between PCRB and varied condition is also fully in- vestigated using different simulation examples. The presented analytical method provides a theoretical base for performance assessment of all kinds of suboptimal estimate algorithms used in practice. | Zhang Jun Zhan Ronghui | 2008 | Journal of Electronics(China)2008，25，1： | 1 |
| 19 | Passive target tracking using marginalized particle filter显示文摘A marginalized particle filtering (MPF) approach is proposed for target tracking under the background of passive measurement.Essentially,the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization,the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter,and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter.Simulation studies are performed on an illustrative example,and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation.Real data test results also validate the effectiveness of the presented method. | Zhan Ronghui Wang Ling Wan Jianwei Sun Zhongkang | 2007 | Journal of Systems Engineering and Electronics2007，18，3： | 0 |
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