Passive source localization in wireless sensor networks (WSNs) is an important field of research with numerous applications in signal processing and wireless communications.
One purpose of a WSN is to determine the position of a signal emitted
from a source. This position is estimated based on received noisy measurements from
sensors (anchor nodes) that are distributed over a geographical area. In most cases,
the sensor positions are assumed to be known exactly, which is not always reasonable.
Even if the sensor positions are measured initially, they can change over time.
Due to the sensitivity of source location estimation accuracy with respect to the
a priori sensor position information, the source location estimates obtained can vary
significantly regardless of the localization method used. Therefore, the sensor position
uncertainty should be considered to obtain accurate estimates. Among the many
localization approaches, signal strength based methods have the advantages of low
cost and simple implementation. The received signal energy mainly depends on the
transmitted power and path loss exponent which are often unknown in practical
scenarios.
In this dissertation, three received signal strength difference (RSSD) based methods
are presented to localize a source with unknown transmit power. A nonlinear
RSSD-based model is formulated for systems perturbed by noise. First, an effective
low complexity constrained weighted least squares (CWLS) technique in the presence
of sensor uncertainty is derived to obtain a least squares initial estimate (LSIE) of
the source location. Then, this estimate is improved using a computationally efficient
Newton method. The Cramer-Rao lower bound (CRLB) is derived to determine the
effect of sensor location uncertainties on the source location estimate. Results are
presented which show that the proposed method achieves the CRLB when the signal
to noise ratio (SNR) is sufficiently high.
Least squares (LS) based methods are typically used to obtain the location estimate
that minimizes the data vector error instead of directly minimizing the unknown
parameter estimation error. This can result in poor performance, particularly in noisy
environments, due to bias and variance in the location estimate. Thus, an efficient
two stage estimator is proposed here. First, a minimax optimization problem is developed
to minimize the mean square error (MSE) of the proposed RSSD-based model.
Then semidefinite relaxation is employed to transform this nonconvex and nonlinear
problem into a convex optimization problem. This can be solved e ciently to obtain
the optimal solution of the corresponding semidefinite programming (SDP) problem.
Performance results are presented which con rm the e ciency of the proposed method
which achieves the CRLB.
Finally, an extended total least squares (ETLS) method is developed for blind
localization which considers perturbations in the system parameters as well as the
constraints imposed by the relation between the observation matrix and data vector.
The corresponding nonlinear and nonconvex RSSD-based localization problem is then
transformed to an ETLS problem with fewer constraints. This is transformed to a
convex semidefinite programming (SDP) problem using relaxation. The proposed
ETLS-SDP method is extended to the case with an unknown path loss exponent.
The mean squared error (MSE) and corresponding CRLB are derived as performance
benchmarks. Performance results are presented which show that the RSSD-based
ETLS-SDP method attains the CRLB for a sufficiently large SNR. / Graduate / 0544 / lohrasbi@uvic.ca
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5614 |
| Date | 27 August 2014 |
| Creators | Lohrasbipeydeh, Hannan |
| Contributors | Gulliver, T. Aaron |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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