The main idea of this thesis is to de ne and formulate the role of bias estimation
in multitarget{multisensor scenarios as a general framework for various measurement
types. After a brief introduction of the work that has been done in this thesis, three
main contributions are explained in detail, which exercise the novel ideas.
Starting with radar measurements, a new bias estimation method that can estimate
o set and scaling biases in large network of radars is proposed. Further,
Cram er{Rao Lower Bound is calculated for the bias estimation algorithm to show
the theoretical accuracy that can be achieved by the proposed method. In practice,
communication loss is also part of the distributed systems, which sometimes can not
be avoided. A novel technique is also developed to accompany the proposed bias
estimation method in this thesis to compensate for communication loss at di erent
rates by the use of tracklets.
Next, bearing{only measurements are considered. Biases in this type of measurement
can be di cult to tackle because the measurement noise and systematic biases
are normally larger than in radar measurements. In addition, target observability
is sensitive to sensor{target alignment and can vary over time. In a multitarget{
multisensor bearing{only scenario with biases, a new model is proposed for the biases
that is decoupled form the bearing{only measurements. These decoupled bias measurements
then are used in a maximum likelihood batch estimator to estimate the
biases and then be used for compensation.
The thesis is then expanded by applying bias estimation algorithms into video
sensor measurements. Video sensor measurements are increasingly implemented in
distributed systems because of their economical bene ts. However, geo{location and
geo{registration of the targets must be considered in such systems. In last part of
the thesis, a new approach proposed for modeling and estimation of biases in a two
video sensor platform which can be used as a standalone algorithm. The proposed
algorithm can estimate the gimbal elevation and azimuth biases e ectively.
It is worth noting that in all parts of the thesis, simulation results of various
scenarios with di erent parameter settings are presented to support the ideas, the
accuracy, mathematical modelings and proposed algorithms. These results show that
the bias estimation methods that have been conducted in this thesis are viable and
can handle larger biases and measurement errors than previously proposed methods.
Finally, the thesis conclude with suggestions for future research in three main
directions. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20028 |
| Date | January 2016 |
| Creators | Taghavi, Ehsan |
| Contributors | Kirubarajan, Thia, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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