Vehicle Tracking Algorithm Based on Observation Feedback and Block Symmetry Particle Filter

This paper proposes a novel particle filter algorithm for vehicle tracking, which feeds observation information back to state model and integrates block symmetry into observation model. In view of the proposal distribution in traditional particle filter without considering the observation data, a new state transition model which takes the observation into account is presented, so that the allocation of particles is more familiar with the posterior distribution. To track the vehicles in background with similar colors or under partial occlusion, block symmetry is proposed and introduced into the observation model. Experimental results show that the proposed algorithm can improve the accuracy and robustness of vehicle tracking compared with traditional particle filter and Kernel Particle Filter.

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A New State Estimation Method with Radar Measurement Missing

Complexity ◽

10.1155/2018/5327637 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Hongjian Wang ◽

Cun Li ◽

Ying Wang ◽

Qing Li ◽

Xicheng Ban

Keyword(s):

State Estimation ◽

State Transition ◽

Estimation Method ◽

The State ◽

Kolmogorov Equation ◽

Transition Model ◽

Observation Data ◽

Adjustment Strategy ◽

Adaptive Adjustment ◽

State Transition Model

This paper describes a method that addresses the transient loss of observations in sea surface target state estimations. A six degrees of freedom swing platform fixed with a MiniRadaScan is used to simulate the loss of observations. The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. An observation data sliding window width adaptive adjustment strategy is proposed that can improve the fitting accuracy of the state transition model. To solve the problem where the weight value of the Gaussian components of the Gaussian mixture filter is not changed in the time update stage while the observation is missing, an adaptive adjustment strategy for the weight is proposed based on the Chapman-Kolmogorov equation, which can improve the estimation precision under the conditions of the missing observation. The simulation test demonstrates the proposed accuracy and real-time performance of the proposed algorithm.

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Enhanced Prognostic Model for Lithium Ion Batteries Based on Particle Filter State Transition Model Modification

Applied Sciences ◽

10.3390/app7111172 ◽

2017 ◽

Vol 7 (11) ◽

pp. 1172 ◽

Cited By ~ 8

Author(s):

Buddhi Arachchige ◽

Suresh Perinpanayagam ◽

Raul Jaras

Keyword(s):

Particle Filter ◽

Lithium Ion Batteries ◽

Prognostic Model ◽

State Transition ◽

Lithium Ion ◽

Transition Model ◽

Model Modification ◽

State Transition Model

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Target Tracking via Particle Filter and Convolutional Network

Journal of Electrical and Computer Engineering ◽

10.1155/2018/5381962 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Hongxia Chu ◽

Kejun Wang ◽

Xianglei Xing

Keyword(s):

Particle Filter ◽

Observation Model ◽

Partial Occlusion ◽

Efficient Design ◽

Complex Environment ◽

Complex Scene ◽

Convolutional Network ◽

Convolutional Networks ◽

Global Representation ◽

Particle Filter Algorithm

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

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Information processing systems. Open systems interconnection. Estelle: a formal description technique based on an extended state transition model

10.3403/00227838u ◽

2015 ◽

Keyword(s):

Information Processing ◽

State Transition ◽

Open Systems ◽

Transition Model ◽

Formal Description ◽

Extended State ◽

Formal Description Technique ◽

Open Systems Interconnection ◽

State Transition Model ◽

Description Technique

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Validation Support Tool to Cross-check the Behavioral Flows on a Requirements Analysis Model using the State Transition Model

2020 International Conference on Computational Science and Computational Intelligence (CSCI) ◽

10.1109/csci51800.2020.00326 ◽

2020 ◽

Author(s):

Hikaru Morita ◽

Saeko Matsuura

Keyword(s):

State Transition ◽

Requirements Analysis ◽

The State ◽

Transition Model ◽

Analysis Model ◽

Support Tool ◽

State Transition Model

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Exploring the Cost Effectiveness of Shared Decision Making for Choosing between Disease-Modifying Drugs for Relapsing-Remitting Multiple Sclerosis in the Netherlands: A State Transition Model

Medical Decision Making ◽

10.1177/0272989x20961091 ◽

2020 ◽

Vol 40 (8) ◽

pp. 1003-1019

Author(s):

Ingrid E. H. Kremer ◽

Mickael Hiligsmann ◽

Josh Carlson ◽

Marita Zimmermann ◽

Peter J. Jongen ◽

...

Keyword(s):

Decision Making ◽

Cost Effectiveness ◽

Shared Decision Making ◽

State Transition ◽

Transition Model ◽

Shared Decision ◽

Relapsing Remitting ◽

State Transition Model ◽

The Cost ◽

Relapsing Remitting Multiple Sclerosis

Background Up to 31% of patients with relapsing-remitting multiple sclerosis (RRMS) discontinue treatment with disease-modifying drug (DMD) within the first year, and of the patients who do continue, about 40% are nonadherent. Shared decision making may decrease nonadherence and discontinuation rates, but evidence in the context of RRMS is limited. Shared decision making may, however, come at additional costs. This study aimed to explore the potential cost-effectiveness of shared decision making for RRMS in comparison with usual care, from a (limited) societal perspective over a lifetime. Methods An exploratory economic evaluation was conducted by adapting a previously developed state transition model that evaluates the cost-effectiveness of a range of DMDs for RRMS in comparison with the best supportive care. Three potential effects of shared decision making were explored: 1) a change in the initial DMD chosen, 2) a decrease in the patient’s discontinuation in using the DMD, and 3) an increase in adherence to the DMD. One-way and probabilistic sensitivity analyses of a scenario that combined the 3 effects were conducted. Results Each effect separately and the 3 effects combined resulted in higher quality-adjusted life years (QALYs) and costs due to the increased utilization of DMD. A decrease in discontinuation of DMDs influenced the incremental cost-effectiveness ratio (ICER) most. The combined scenario resulted in an ICER of €17,875 per QALY gained. The ICER was sensitive to changes in several parameters. Conclusion This study suggests that shared decision making for DMDs could potentially be cost-effective, especially if shared decision making would help to decrease treatment discontinuation. Our results, however, may depend on the assumed effects on treatment choice, persistence, and adherence, which are actually largely unknown.

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