Erratum to “VLSI based fuzzy logic controller enabled adaptive interactive multiple model for target tracking” [INTEGRATION, the VLSI journal 35 (2003) 1–10]

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.

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Fuzzy Logic Based Interactive Multiple Model Fault Diagnosis for PEM Fuel Cell Systems

Discrete Time Systems ◽

10.5772/13994 ◽

2011 ◽

Cited By ~ 1

Author(s):

Yan Zhou ◽

Dongli Wang ◽

Jianxun Li ◽

Lingzhi Yi ◽

Huixian Huang

Keyword(s):

Fuzzy Logic ◽

Fuel Cell ◽

Fault Diagnosis ◽

Pem Fuel Cell ◽

Multiple Model ◽

Cell Systems ◽

Interactive Multiple Model

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Target tracking in mobile robot under uncertain environment using fuzzy logic controller

2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) ◽

10.1109/eecsi.2017.8239079 ◽

2017 ◽

Cited By ~ 3

Author(s):

Ade Silvia Handayani ◽

Tresna Dewi ◽

Nyayu Latifah Husni ◽

Siti Nurmaini ◽

Irsyadi Yani

Keyword(s):

Fuzzy Logic ◽

Mobile Robot ◽

Target Tracking ◽

Fuzzy Logic Controller ◽

Uncertain Environment

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Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking

Symmetry ◽

10.3390/sym11060767 ◽

2019 ◽

Vol 11 (6) ◽

pp. 767

Author(s):

Jian Wan ◽

Peiwen Ren ◽

Qiang Guo

Keyword(s):

Fuzzy Logic ◽

Simulation Analysis ◽

Model Simulation ◽

Multiple Model ◽

Fuzzy Logic Algorithm ◽

Error Covariance ◽

Speed Tracking ◽

Model Probability ◽

Interactive Multiple Model ◽

Tracking Model

Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the “snake-like” maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage–Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).

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