Global Positioning System Reference
In-Depth Information
that of the various PLUs, a management and fusion scheme is computed such that the scheme
produces a location estimate that meets the task requirements.
To overcome the problems of this layer, first of all, PLU estimates should be time-stamped
as close as possible to a common time base. Of course the allowable synchronization error
would depend on factors such as the speed of the vehicle relative to the PLU response time. It
is also affected by the system's desired spatial precision and detection frequency. The tighter
time synchronization is achieved with respect to the common time base, the greater precision
is possible in the tracking of the vehicle.
Second of all, since the fusion process is task driven, an optimal fusion strategy is the one that
achieves the target accuracy and integrity within the constraints of the task deadline. This
gives rise to the challenge of optimal estimate fusion and reliability aggregation. Both fuzzy
reasoning and evidential reasoning are a tentative tools to be investigated as the bases for
constructing the meta-fusion model. Fuzzy reasoning can be used for representing uncertainty
in the estimates as well as for representing linguistic task requirements. Since some PLUs may
employ probabilistic (Bayesian) estimators, it will be interesting to study how probabilistic
estimates and fuzzy estimates are represented in a unified uncertainty framework.
Bayesian theory based fusion techniques have been evolving in fields such as process control,
target tracking and object recognition. Nonetheless, effective fusion performance can only
be achieved if adequate and appropriate priori and conditional probabilities are available.
Although, at least in some situations, assumptions can be made with respect to priori
and posteriori probabilities, these assumptions can turn to be unreasonable in many other
situations, especially if we are to allow for non-probabilistic estimators in the PLU layer. One
possible solution is using the Dempster-Shafer (DS) evidence theory as an extension to the
Bayes theory. DS belief and plausibility functions can be used to quantify evidence and unify
uncertainty of the PLU estimates. DS evidence theory can also model how the uncertainty of
a given location estimate diminishes as pieces of evidence accumulate during the localization
process. One important aspect of this theory is that reasoning or decision making can be
carried out with incomplete or conflicting pieces of evidence - a reality that is quit common
in localization problems.
10. Conclusions
In this chapter, a variety of reported localization techniques are presented and classified based
on the type of the measurement of the location information used.
Although, techniques that incorporate fusion of motion sensory data with GPS localization
have demonstrated improvement in performance, there are still situations that can have a
negative effect on their localization accuracy. Incremental localization errors in motion-sensor
data and the multipath effect in urban canyon environments contribute significantly to such
location estimate errors, which necessitates augmenting the initial location data with other
sources of location information in order to overcome these shortcomings.
Digital maps and visual features enhance GPS-DR localization by recognizing landmarks in
the surrounding environment and matching them with others in a reference GIS map. A key
problem associated with this scheme is that the landmark segmentation process is complex
and ill conditioned process.
Multi-level fusion schemes are promising as they employ multiple location measurement
phenomena. However, these schemes have given birth to new challenges in the localization
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