FHWS/OTHER2017
Archived
3
0
Fork 0
Code Issues 4 Pull Requests Releases Wiki Activity

current TeX

Browse Source
This commit is contained in:
FrankE
2017-05-02 18:31:36 +02:00
parent 47def9ad90
commit 1b24977a95
3 changed files with 72 additions and 61 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
tex/chapters/conclusion.tex
View File

@@ -4,7 +4,7 @@
As denoted within the previous evaluations and discussions, the accuracy of
indoor localization systems based on \docWIFI{} depends on a manifold
of parameters and even minor adjustments can yield huge improvements.
of parameters and even minor adjustments can yield visible improvements.
Depending on required accuracy and acceptable setup- and maintenance times,
several approaches are conceivable:
@@ -13,25 +13,26 @@
is a viable choice for many situations.
However, when combined with (particle) filtering, a heavily constrained
movement model might be a potential issue, as it might get stuck when
movement model might be a potential issue, as it can get stuck when
sensor observations or model predictions are too erroneous.
Using a small number of reference measurements will already suffice
to improve such errors. Furthermore it also removes the need for prior knowledge
like transmitter locations, as those parameters can be estimated via optimization.
Using a small number of reference measurements to optimize the model parameters will already suffice
to improve such errors. Furthermore, it also removes the need for prior knowledge
about transmitter locations, as those can be estimated via optimization.
For the best accuracy, more complex signal strength propagation models
are required which, in turn, demand for more reference measurements.
are required, which in turn demand for more reference measurements.
%
However, while using a several instances of a simple propagation model
for different regions within a building is able to decrease the estimation
error, this approach might require prior guessing of where to place those regions
and is still unable to approximate all signal strength variations within the building.
error, this approach might require prior guessing of where to place those regions.
As indicated by the error plots, just using one model for every floor within the building
seems to be a viable alternative.
More complex models that include information about walls and other obstacles should
be able to improve the situation at the cost of additional computation.
More complex models, that include information about walls and other obstacles, should
be able to reduce the remaining maximum error, which remains for some locations, at the cost of additional computations.
Special data-structures for pre-computation combined with online interpolation might
be a viable choice for utmost accuracy while still being able to run on
be a viable choice for utmost accuracy that is still able to run on
a commodity smartphone in realtime.
While we were able to improve the performance of the \docWIFI{} sensor component,