The main idea of this thesis is to formulate the smooth variable structure filter (SVSF) for target tracking applications in the presence of measurement origin uncertainty. Tracking, by definition is the recursive estimation of the states of an unknown target from indirect, inaccurate and uncertain measurements. The measurement origin uncertainty introduces the data association problem to the tracking system.
The SVSF estimation strategy was first presented in 2007. This filter is based on sliding mode concepts formulated in a predictor-corrector form. Essentially, the SVSF uses an existence subspace and smoothing boundary layer to bind the estimated state trajectory to within a subspace around the true trajectory. The SVSF is demonstrated to be robust to modeling uncertainties and provide extra measures of performance such as magnitude of the chattering signal. Therefore, with respect to specific nature of car tracking problems that involves modeling uncertainty, it was hypothesized that a robust estimation strategy such as the SVSF, would improve the performance of the tracking system and give more robust tracking results. Also, having the extra information provided by the SVSF strategy, i.e. the chattering magnitude signal, would lead to algorithms that could better account for measurement origin uncertainty in the context of the data association process. Further to these hypotheses, this research has focused on investigating the performance of the SVSF in the target tracking problems, advancing the development of the SVSF, and employing its characteristics to deal with data association problems.
The performance of the SVSF, in its current form, can be improved when there is fewer measurements than states by using its error covariance in target tracking.
As the first contribution in this research, the SVSF is formulated in the context of target tracking in clutter and combined with data association algorithms, resulting in the SVSF-based probabilistic data association (PDA) and joint probabilistic data association (JPDA) for non-maneuvering and maneuvering targets. The results are promising in the tracking scenarios with modeling uncertainties. Therefore, the thesis is then expanded by generalizing the covariance of the SVSF for the cases where the number of measurements is less than the number of states. The generalized covariance formulation is then used to derive a generalized variable boundary layer (GVBL) SVSF. This new derivation gives an estimation method that is optimal in the MMSE sense and in the meantime preserves the robustness of the SVSF. The proposed algorithm improves the performance measures and makes a more reliable tracking algorithm.
This thesis explores the hypothesis that multiple target tracking performance can be substantially improved by including chattering information from SVSF-based filtering in the data association method. A Bayesian framework is used to formulate a new set of augmented association probabilities which include the chattering information. The simulation and experimental results demonstrate that the proposed augmented probabilistic data association improves the performance of the tracking system including maneuvering cars, in particular for highly cluttered environments.
The derived methods are applied on simulations and also on real data from an experimental setup. This thesis is made up of a compilation of papers that include three conference papers and three journal papers. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/19009 |
| Date | January 2016 |
| Creators | Attari, Mina |
| Contributors | Habibi, Saeid, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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