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dc.contributor.advisorMulgrew, Bernieen
dc.contributor.advisorThompson, Johnen
dc.contributor.authorLi, Zeyuanen
dc.date.accessioned2018-07-18T10:08:38Z
dc.date.available2018-07-18T10:08:38Z
dc.date.issued2018-07-04
dc.identifier.urihttp://hdl.handle.net/1842/31356
dc.description.abstractThe subject of this thesis is the development of localization algorithms for target localization in wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS (QRSS) measurements. In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many existing works on RSS localization assumes that the shadowing components are uncorrelated. However, here, shadowing is assumed to be spatially correlated. It can be shown that localization accuracy can be improved with the consideration of correlation between pairs of RSS measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function, a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative technique based on Newtons method is utilized to give a solution. Numerical simulations show that the proposed algorithms achieves better performance than existing algorithms with reasonable complexity. In chapter 4, target localization with an unknown path loss model parameter is investigated. Most published work estimates location and these parameters jointly using iterative methods with a good initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function. By combining PSO with a consensus algorithm, the centralized estimation problem is extended to a distributed version so that can be implemented in distributed WSN. Although suboptimal, the distributed approach is very suitable for implementation in real sensor networks, as it is scalable, robust against changing of network topology and requires only local communication. Numerical simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound (CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with respect to the centralized PSO. In chapter 5, a distributed gradient algorithm for RSS based target localization using only quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Simulations show that the performance of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data.en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionZeyuan Li and P. Chung, ”Weighted Least Squares Solution for RSS based Localization in Correlated Shadowing,” In Signal Processing Systems (SiPS), 2014 IEEE Workshop on, Belfast, 2014, pp. 80-84.en
dc.relation.hasversionZeyuan Li, Pei-Jung Chung, Bernard Mulgrew, Distributed target localization using quantized received signal strength, In Signal Processing, Volume 134, 2017, Pages 214-223.en
dc.subjectQuantized RSS measurementsen
dc.subjectRSS measurementsen
dc.subjecttarget localizationen
dc.subjectpath loss model parametersen
dc.subjectpath loss exponenten
dc.subjectparticle swarm optimizationen
dc.subjectdistributed algorithmsen
dc.titleTarget localization using RSS measurements in wireless sensor networksen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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