On applicability of the interacting multiple-model approach to state estimation for systems with sojourn-time-dependent Markov model switching

1996 ◽  
Vol 41 (1) ◽  
pp. 136-140 ◽  
Author(s):  
A.I. Petrov ◽  
A.G. Zubov
Keyword(s):  
Markov Model ◽  
State Estimation ◽  
Sojourn Time ◽  
Time Dependent ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Approach

State estimation for systems with sojourn-time-dependent Markov model switching

1991 ◽  
Vol 36 (2) ◽  
pp. 238-243 ◽  
Author(s):  
L. Campo ◽  
P. Mookerjee ◽  
Y. Bar-Shalom
Keyword(s):  
Markov Model ◽  
State Estimation ◽  
Sojourn Time ◽  
Time Dependent

Vehicle and road state estimation using interacting multiple model approach

2006 ◽  
Vol 44 (sup1) ◽  
pp. 750-758 ◽  
Author(s):  
H. Tsunashima ◽  
M. Murakami ◽  
J. Miyataa
Keyword(s):  
State Estimation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Approach

scholarly journals Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2332 ◽  
Author(s):  
Vincent Judalet ◽  
Sébastien Glaser ◽  
Dominique Gruyer ◽  
Saïd Mammar
Keyword(s):  
Autonomous Vehicle ◽  
Fault Detection And Isolation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Actuator Faults ◽  
Robust Identification ◽  
The Road ◽  
Driving Assistance ◽  
Experimental Vehicle ◽  
Model Approach

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

Maneuvering Car Tracking Using the Interacting Multiple Model and the Dynamic 2nd-Order SVSF Method

2015 ◽  
Author(s):  
Mina Attari ◽  
Hamed Hossein Afshari ◽  
Saeid Habibi
Keyword(s):  
State Estimation ◽  
Sliding Mode ◽  
Estimation Method ◽  
Variable Structure ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Structure Control ◽  
Position Data ◽  
Car Driving ◽  
Tracking Strategy

Car tracking algorithms have recently found a major role in intelligent automotive applications. They are mainly based on the state estimation techniques to solve the maneuvering car tracking problems. The dynamic 2nd-order SVSF method is a novel robust state estimation method that is based on the variable structure control theory. It benefits from the accuracy, robustness, and chattering suppression properties of second-order sliding mode systems for robust state estimation. The main contribution of this paper is to present and implement a new tracking strategy that is a combination of the dynamic 2nd-order SVSF with the IMM filter. It benefits from the robust performance of the dynamic 2nd-order SVSF and the switching property of the IMM filter. This strategy is simulated and examined under several car driving patterns and experimental position data that are captured by a GPS device. The robustness and efficiency of this strategy is then compared with the Kalman filter-based counterparts.

scholarly journals Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 111536-111552
Author(s):  
Zhen Tian ◽  
Ming Cen ◽  
Yinguo Li ◽  
Hao Zhu
Keyword(s):  
Markov Model ◽  
Sojourn Time ◽  
Vehicle Tracking ◽  
Time Dependent ◽  
Ground Vehicle
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