Tracking systems are already encountered in everyday life in numerous applications, but
many algorithms from the existing literature rely on assumptions that do not always
hold in realistic scenarios, or can only be applied in niche circumstances. Therefor this
thesis is motivated to develop new approaches that relax assumptions and restrictions,
improve tracking performance, and are applicable in a broad range of scenarios. In
the area of terrain-aided tracking this an algorithm is proposed to track targets using
a Gaussian mixture measurement distribution to better represent multimodal distributions
that can arise due to terrain conditions. This allowed effective use in a wider
range of terrain conditions than existing approaches, which assume a unimodal Gaussian
measurement distribution. Next, the problem of estimating and compensating for
sensor biases is considered in the context of terrain-aided tracking. Existing approaches
to bias estimation cannot be easily reconciled with the nonlinear converted measurement
model applied in terrain-aided tracking. To address this, a novel efficient bias estimation
algorithm is proposed that can be applied to a wide range of measurement models
and operational scenarios, allowing for effective bias estimation and measurement compensation
to be performed in situations that cannot be handled by existing algorithms.
Finally, to address scenarios where converted measurement tracking is not possible or
desired, the problem of sensor motion compensation when tracking in pixel coordinates
is considered. Existing approaches compensate for sensor motion by transforming state
estimates between frames, but are only able to achieve partial transformation of the
state estimate and its covariance matrix. This thesis proposes a novel algorithm used to
transform the full state estimate and its covariance matrix, improving tracking performance
when tracking with a low frame rate and when tracking targets moving with a
nearly coordinated turn motion model. Each of the proposed algorithms are evaluated
in several simulated scenarios and compared against existing approaches and baselines
to demonstrate their efficacy. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27799 |
| Date | January 2022 |
| Creators | Schonborn, David |
| Contributors | Kirubarajan, Thia |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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