- Title
- Bayesian methodologies for extended target tracking
- Creator
- Bartlett, Nathan
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2020
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Since its initial conception in the mid 1960's, multiple point-target tracking has been at the heart of many applications; spanning from surveillance and air-traffic control, to medicine and econometrics. In modern times, a generalisation of point-target tracking, referred to as extended target tracking has become increasingly popular, due to its ability to handle the generation of multiple measurement per target in each time-step. Today, multiple extended-target tracking is one of the most crucial components in vehicle autonomy - enabling for autonomous vehicles to avoid imminent collision with moving objects in the scene. The focus of this thesis is on the tracking of multiple extended targets in a robust and computationally efficient manner. One of the key developments of this work is the proposal of a new class of state transition models that afford closed-form predictions for the tracking of extended targets. This model builds upon the immensely popular random matrix model, and employs a non-central inverse Wishart distribution to represent the state transition density of the target extent. Importantly, this action results in a prediction update that experiences less overconfidence in its estimation quality than previous works, and offers an additional tuning parameter to model unforeseen deformations in the target extent. This proposed prediction update is then generalised to obtain an algorithm that can track extended targets undergoing kinematic state dependent rotations - no matter the size of the turn-rate variance. To compliment the above prediction schemes, we additionally derive a new correction update for the factorised random matrix model; which utilises an alternative conditional expectation to produce better estimates of the target extent. To take full advantage of this correction update, a new multiple model approach is derived; which, by additionally considering the above generalised prediction, results in an extended-target tracking filter with superior tracking performance than state-of-the-art alternatives. To combat the problem of combinatorial data association, a new partitioning scheme for multiple extended target tracking is also derived in this work. A key innovation is to employ Gibbs sampling to obtain a subset of high-quality partitions from a Dirichlet process Gaussian mixture model. This partitioning scheme is integrated into the Gamma Gaussian inverse Wishart Poisson multi-Bernoulli mixture filter, and shown to produce better overall tracking performance than previous works. Moreover, it is shown that the proposed partitioning scheme possesses the following highly desirable characteristics: it works equally well for spatially close and distant targets; it does not assert any additional assumptions upon the spatial distribution of each measurement; it is less sensitive to the quality of the predicted hypotheses; and finally, the sampling distribution is guaranteed to converge to the true posterior distribution.
- Subject
- extended target tracking; Bayesian filtering; random matrix; inverse Wishart; random finite set
- Identifier
- http://hdl.handle.net/1959.13/1422839
- Identifier
- uon:37881
- Rights
- Copyright 2020 Nathan Bartlett
- Language
- eng
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
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| View Details Download | ATTACHMENT01 | Thesis | 7 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 519 KB | Adobe Acrobat PDF | View Details Download |