An Interacting Multiple-Model-Based Algorithm for Driver Behavior Characterization Using Handling Risk

2019 ◽  
Vol 20 (12) ◽  
pp. 4308-4317 ◽  
Author(s):  
Sanghyun Hong ◽  
Jianbo Lu ◽  
Smruti R. Panigrahi ◽  
Jonathan Scott ◽  
Dimitar P. Filev
Keyword(s):  
Driver Behavior ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Model Based

IMM-UKF-TFS Model-based Approach for Intelligent Navigation

2013 ◽  
Vol 66 (6) ◽  
pp. 859-877 ◽  
Author(s):  
M. Malleswaran ◽  
V. Vaidehi ◽  
S. Irwin ◽  
B. Robin
Keyword(s):  
Kalman Filter ◽  
Constant Velocity ◽  
Unscented Kalman Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
State Variables ◽  
Constant Acceleration ◽  
Positional Accuracy ◽  
Model Based ◽  
Novel Approach

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

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.

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