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Swathi Srinivasan Thesis.pdf (853.25 KB)

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State Estimation based on Nested Particles Filter

SRINIVASAN, SWATHI
http://rave.ohiolink.edu/etdc/view?acc_num=csu1376257061

Abstract Details

2013, Master of Science in Chemical Engineering, Cleveland State University, Fenn College of Engineering.
In reality many processes are nonlinear and in order to have a knowledge about the true process conditions, it is important to make decisions based on the state of the system. Process measurements such as pressure, temperature, and pH, are available at time instances and this information is necessary in order to obtain the state of the system. Filtering is a state estimation technique by which the estimate is obtained at a time instant, given the process measurements at their respective time instances.Several filters have been developed so far for the estimation of the states of the system. Kalman filters are the optimal filter algorithms used for linear state and measurement models. Approximations are made to this algorithm in order to apply to non-linear systems. Particle filter (PF) is one such approximation made to the Kalman filtering technique. It involves drawing a set of samples or particles from the state of the system. It works on the principle of importance sampling, where, the samples are derived from a probability density function which is similar to the state model. The particles are resampled according to their weights in order to determine the estimate. Taking into account the difficulties in particle filtering technique, a nested particles filter (NPF) was developed. NPF works in such a way that there is a set of particles under each sample of the original particle filter, and from these nest of samples the transition prior is updated using an extended Kalman particle filter (EKPF). The idea of nested particle filter was developed from the unscented particles filter (UPF), which uses the concept of local linearization to develop the importance density. Better importance densities are formulated in this case through which better posteriori are obtained. It is important to note that the update of the NPF can be done with any suboptimal nonlinear filter available. This thesis work is based on developing the NPF with a direct sampling particle filter (DSPF) update. Some modifi cations are made to the unscented particle filter algorithm. The first part of the thesis is to update to NPF with an ensemble Kalman filer update (EnKF). One mathematical example is used to explain the working of the lter and this is compared with the working of NPF with a DSPF update.
Sridhar Ungarala, PhD (Advisor)
Jorge Gatica, PhD (Committee Member)
Rolf Lustig, DRE (Committee Member)
Chemical Engineering

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Citations

  • SRINIVASAN, S. (2013). State Estimation based on Nested Particles Filter [Master's thesis, Cleveland State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=csu1376257061

    APA Style (7th edition)

  • SRINIVASAN, SWATHI. State Estimation based on Nested Particles Filter. 2013. Cleveland State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=csu1376257061.

    MLA Style (8th edition)

  • SRINIVASAN, SWATHI. "State Estimation based on Nested Particles Filter." Master's thesis, Cleveland State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=csu1376257061

    Chicago Manual of Style (17th edition)

csu1376257061
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This open access ETD is published by Cleveland State University and OhioLINK.