The traditional multiplemodel cardinalized probability hypothesis density (MMCPHD) filter uses a fixed model *** all targets. It is clearly inefficient and may cause the decrease of performance in the situations wher...
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The traditional multiplemodel cardinalized probability hypothesis density (MMCPHD) filter uses a fixed model *** all targets. It is clearly inefficient and may cause the decrease of performance in the situations where the size of the model set is large and the scene is complicated. This paper presents a variable structure multiple model (VSMM) version of the Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter based on the expected mode augmentation (EMA) for the multiple maneuvering target tracking. The GMCPHD filter which is suitable for the VSMM is derived, thus different model sets can be used for different targets. Then EMA algorithm which is an efficient model set adaptation method is introduced and applied to the VSMM version of the GMCPHD (VSMM-GMCPHD) filter. This method costs less computational time and has better estimate precision than that of the traditional multiplemodel version of GMCPHD (MM-GMCPHD) filter. The gating technique is utilized to further improve the computational efficiency. Simulation results verify the performance of the proposed methods. The estimate precision of VSMM-GMCPHD filters with and without the gate are almost the same, however the former one is more cost-effective. Both of the two VSMM-GMCPHD filters outperform the MM-GMCPHD filter. (C) 2017 Published by Elsevier B.V.
This paper considers the problem of maneuvering target tracking for distributed sensor networks. Consensus-based multiplemodel (MM) filters are effective methods to deal with this problem. However, the model set of t...
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This paper considers the problem of maneuvering target tracking for distributed sensor networks. Consensus-based multiplemodel (MM) filters are effective methods to deal with this problem. However, the model set of these filters is fixed. When the scene is complicated and the model set for MM methods is large, these methods may become inefficient and performance may decrease while the variable structure multiple model (VSMM) method can handle it well. The VSMM version of consensus filter based on the expected mode augmentation (EMA) is presented in this paper to track maneuvering target for distributed sensor networks. The distributed consensus filter for VSMM is presented first and it runs parallel consensus on both the mode probability and the mode-matched probability density function (PDF). Then the EMA algorithm is used to determine the model set utilized at each time step. Simulation results demonstrate the superiority of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.
The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiplemodel algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismat...
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The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiplemodel algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking *** adaptive grid-expected-mode augmentation variable structure multiple model algorithm is *** adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model ***,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is *** the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.
This paper studies the problem of maneuvering target tracking, and focuses on the maneuvering target tracking algorithm under the framework of autonomous multiplemodel, cooperative multiplemodel and variable multipl...
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ISBN:
(纸本)9781728123110
This paper studies the problem of maneuvering target tracking, and focuses on the maneuvering target tracking algorithm under the framework of autonomous multiplemodel, cooperative multiplemodel and variablemultiplemodelstructure, and deduces the processing steps of the typical tracking algorithm under the above three types of multi-modelstructure framework. Combined with the practical application requirements of radar networking system, a feasible method for spatial registration and time registration is proposed. The effectiveness of multi-model tracking algorithm is verified by constructing radar network simulation environment. The application of multi-model tracking algorithm in radar network system is sorted out and summarized. The conclusions given in this paper have guiding significance for the engineering implementation of radar networking system.
For nonlinear problem in target tracking, on the basis of maneuver turn model, the AGIMMCKF-algorithm is proposed, which is a variablemultiplemodel algorithm that combined cubature kalman filter(CKF) with adaptive g...
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ISBN:
(纸本)9781538604854
For nonlinear problem in target tracking, on the basis of maneuver turn model, the AGIMMCKF-algorithm is proposed, which is a variablemultiplemodel algorithm that combined cubature kalman filter(CKF) with adaptive grid(AG). CKF as a filter, the algorithm using grid center and the adjustment of the minimum distance between grid to change turn model set adaptively so as to effectively track targets, and then compared with IMMCKF. The simulation results show that the proposed algorithm has higher tracking accuracy and stability compared with IMMCKF algorithm in the absence of an increase in running time.
This paper studies the algorithm of the adaptive grid and fuzzy interacting multiplemodel （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structuremultiplemodel （FSMM） algo...
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This paper studies the algorithm of the adaptive grid and fuzzy interacting multiplemodel （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structuremultiplemodel （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiplemodel （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiplemodel algorithm, and as a result is suitable for enineering apolications.
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