Performance comparison of particle swarm optimization and extended Kalman filter methods for tracking in non-linear dynamic systems

Amor, Nahla Ben; Chebbi, Souad

doi:10.1109/cadiag.2017.8075641

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…PMCMC methods use a set of particles to explore the parameter space, which can help to avoid getting stuck in local optima and improve the overall exploration of the parameter space [87]. PMCMC methods can handle non-Gaussian distributions associated with measurement error and associated with the perturbations in system evolution, which is useful when working with complex systems and many types of non-normally distributed epidemiological data [88,89]. PMCMC methods can be particularly valuable when working with time series data, as they can help to capture the dynamics of the system over time and can predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Zahan,

Mikuliak,

Osgood

2024

Preprint

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Suicide dynamics form a complex system deeply influenced by interrelated factors, necessitating advanced methodologies for comprehensive analysis. This study pioneers the application of Particle Markov Chain Monte Carlo (PMCMC) methods in suicide research, recognized for their enhanced sampling efficiency over traditional techniques in complex, high-dimensional models. PMCMC is particularly adept at handling non-Gaussian distributions and navigating parameter spaces efficiently, preventing the entrapment in local optima. Utilizing PMCMC, our research simulated a broad spectrum of potential outcomes, clarifying the probable scenarios and their likelihoods, thus enriching the predictive accuracy and understanding of suicide dynamics. These methods facilitated the exploration of dynamic interactions among key risk factors such as mental health issues, trauma, substance use, social isolation, and access to harmful means, whose complex interplays challenge predictive modeling. The study extends previous applications of PMCMC in complex systems like H1N1, opioid crises, and COVID-19 to the domain of suicide, suggesting its potential in enhancing decision-making and intervention strategies. However, limitations due to the model's simplified assumptions and the specificity of the data to certain populations underscore the necessity for broader application to validate findings across varied demographics. In summary, while PMCMC offers robust capabilities for the dynamic modeling of suicide, it requires careful parameter selection and consideration of computational demands. Future research should continue to leverage this approach in more complex settings, enhancing our ability to predict and mitigate suicide risks effectively.

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Section: Discussionmentioning

confidence: 99%

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Zahan,

Mikuliak,

Osgood

2024

Preprint

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“…After the object of interest is detected, the goal is to analyze their behavior that sometimes can be done by tracking them. Usually, particle filters and Kalman filters are employed for the purpose of tracking objects in IVS systems [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Particle-filter-based intelligent video surveillance system

Tsai

Wang

et al. 2016

2016 International Conference on System Science and Engineering (ICSSE)

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In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience.

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Design of Parallel Algorithm for Kalman Filter on SW26010 Processors

Yang

2021

J. Phys.: Conf. Ser.

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Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, the financial industry, and so on. On the Sunway TaihuLight platform, we present an improved Kalman filter parallel algorithm which is according to the new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for the KF algorithm based on a seven-level pipeline of the SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve the parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five different data sets, and is also improved compared to the algorithm on GPU.

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Performance comparison of particle swarm optimization and extended Kalman filter methods for tracking in non-linear dynamic systems

Cited by 6 publications

References 10 publications

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Developing a Tool to Assess the Impact of Simulated Intervention Strategies on Suicide and Suicidal Behaviours in Canada: A Dynamic Modelling & Machine Learning Approach

Particle-filter-based intelligent video surveillance system

Design of Parallel Algorithm for Kalman Filter on SW26010 Processors

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