Indoor and Outdoor Location Estimation in Large Areas Using Received Signal Strength
Abstract
Location estimation when deployed on wireless networks supports a range of services
including user tracking and monitoring, health care support and push and pull marketing.
The main subject of this thesis is improving indoor and outdoor location estimation
accuracy using received signal strength (RSS) from neighbouring base stations (BSs) or
access points (APs), without using the global positioning system (GPS) or triangulation
methods. For the outdoor environment, state-of-the-art deterministic and probabilistic
algorithms are adapted to exploit principal components (PCs) and clustering. The
accuracy is compared with K-nearest neighbour (KNN) algorithms using different partitioning
models. The proposed scheme clusters the RSS tuples based on deviations from
an estimated RSS attenuation model and then transforms the raw RSS in each cluster
into new uncorrelated dimensions, using PCs. As well as simple global dimensionality
reduction using PCs, the data reduction and rotation within each cluster improves estimation
accuracy because a) each cluster can model the different local RSS distributions
and b) it efficiently preserves the RSS correlations that are observed (some of which are
substantial) in local regions and which independence approximations ignore. Different
simulated and real environments are used for the comparisons. Experimental results
show that positioning accuracy is significantly improved and fewer training samples are
needed compared with traditional methods. Furthermore, a technique to adjust RSS
data so that radio maps collected in different environmental conditions can be used together
to enhance accuracy is also demonstrated. Additionally, in the radio coverage
domain, a non-parametric probability approach is used for the radio reliability estimation
and a semi-supervised learning model is proposed for the monitoring model training
and evolution according to real-time mobile users’ RSS feedback.
For the indoor environment, an approach for a large multi-story indoor location estimaiii
tion using clustering and rank order matching is described. The accuracies using WiFi
RSS alone, cellular GSM RSS alone and integrated WiFi and GSM RSS are presented.
The methods were tested on real indoor environments. A hierarchical clustering method
is used to partition the RSS space, where a cluster is defined as a set of mobile users
who share exactly the same strongest RSS ranking set of transmitters. The experimental
results show that while integrating of WiFi RSS with GSM RSS creates a marginal
improvement, the GSM data can be used to ameliorate the loss of accuracy when APs
Authors
Li, KejiongCollections
- Theses [4125]