scholarly journals Joint State Estimation and System Classification Using Particle Filtering and Interacting Multiple-Model for Maneuvering Target Tracking

2019 ◽  
Vol 12 (4) ◽  
pp. 15-22
Author(s):  
S. Nasorallah Hosseini ◽  
Mohammad Haeri ◽  
Hamid Khaloozadeh ◽  
◽  
◽  
...  
Keyword(s):  
State Estimation ◽  
Target Tracking ◽  
Particle Filtering ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Joint State

scholarly journals IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

2017 ◽  
Author(s):  
Hua Liu ◽  
Wen Wu
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Scenario Simulation ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Spherical Simplex

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

scholarly journals Modular Interacting Multiple Model Based on Extended Viterbi Algorithm for Maneuvering Target Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Xia Liu ◽  
Fei Long ◽  
Wenjie Zhang ◽  
Lu Guo
Keyword(s):  
Target Tracking ◽  
Viterbi Algorithm ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Markov Jump ◽  
Maneuvering Target ◽  
Model Based ◽  
Time Simulation ◽  
Markov Jump Linear Systems

A new maneuvering target tracking algorithm is investigated, which is modeled as a class of Markov jump linear systems (MJLS). Drawing on the experience of combination idea of the extended Viterbi algorithm (EV) and the interacting multiple model algorithm (IMM), a modular interacting multiple model based on extended Viterbi (MIMMEV) is presented. The MIMMEV algorithm consists ofNindependent interacting multiple model-extended Viterbi (IMM-EV). Furthermore, these IMM-EV filters are independent and working in parallel in the MIMMEV algorithm. According to the derived probability, the estimated state of every moment is the weighted sum of each estimator at the corresponding time. Simulation results demonstrate that the proposed algorithm improves the tracking precision and reduces the computational burden compared with traditional IMM and IMM-EV.

Maneuvering Target Tracking Based on Radar/ESM Sensors

2015 ◽  
Vol 713-715 ◽  
pp. 672-675 ◽  
Author(s):  
Xing Xiu Li ◽  
Pan Long Wu
Keyword(s):  
Dynamic System ◽  
Target Tracking ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Heterogeneous Sensors ◽  
System Models ◽  
Maneuvering Target ◽  
Blue Filter ◽  
Simulation Results

A novel interacting multiple model based on BLUE filter (IMM-BLUE) for tracking a maneuvering target using radar/ESM heterogeneous sensors is presented in this paper. Under the architecture of the proposed algorithm, the interacting multiple model (IMM) deals with the model switching, while the BLUE filter accounts for non-linearity in the dynamic system models. The simulation results show that the presented IMM-BLUE has higher tracking precision than the IMM-DCM, and IMM-EKF.

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