{"id":{"repo_id":"brazil-uerj","oai_identifier":"oai:pantheon.ufrj.br:11422/6081"},"canonical_url":"https://search.dev.ndltd.org/etd/brazil-uerj/oai:pantheon.ufrj.br:11422/6081","repository":{"repo_id":"brazil-uerj","name":"Brazil UERJ","base_url":"https://pantheon.ufrj.br/oai/request"},"display":{"title":"Contributions to radio frequency indoorpositioning and through-the-wall mapping","abstract":"The first part of this work introduces a novel machine-learning based location engine aiming at improving floor identification accuracy in radio frequency (RF) multi-floor indoor positioning while preserving a low two-dimensional positioning error. The location engine also reduces the position fix computational complexity. A large database of RF samples was collected in a 13-storey building to evaluate the proposal. The second part of this work studies in detail radio tomographic imaging, also referred to as RF-based through-the-wall mapping (TWM). Four different reconstruction algorithms – two projective and two algebraic – are compared using a path-loss model corrupted by Rayleigh noise in a parallel-beam acquisition geometry. After that, the thesis proposes applying the Finite Element Method (FEM) to simulate several parallel-beam geometry RF TWM setups, providing a more accurate simulation model. The meshing parameters of the FEM model geometry have been optimized, enabling a significant computational cost reduction while preserving accuracy. Reconstruction of two floor maps is carried out using the FEM model with different sampling rates, operational frequencies, and antenna models. Finally, a multi-sensor circular acquisition geometry (MCG) is defined to reduce the time required to acquire the RF samples in comparison to the parallel-beam geometry. The MCG scheme is evaluated using the proposed FEM framework.","abstract_html":"The first part of this work introduces a novel machine-learning based location engine aiming at improving floor identification accuracy in radio frequency (RF) multi-floor indoor positioning while preserving a low two-dimensional positioning error. The location engine also reduces the position fix computational complexity. A large database of RF samples was collected in a 13-storey building to evaluate the proposal. The second part of this work studies in detail radio tomographic imaging, also referred to as RF-based through-the-wall mapping (TWM). Four different reconstruction algorithms – two projective and two algebraic – are compared using a path-loss model corrupted by Rayleigh noise in a parallel-beam acquisition geometry. After that, the thesis proposes applying the Finite Element Method (FEM) to simulate several parallel-beam geometry RF TWM setups, providing a more accurate simulation model. The meshing parameters of the FEM model geometry have been optimized, enabling a significant computational cost reduction while preserving accuracy. Reconstruction of two floor maps is carried out using the FEM model with different sampling rates, operational frequencies, and antenna models. Finally, a multi-sensor circular acquisition geometry (MCG) is defined to reduce the time required to acquire the RF samples in comparison to the parallel-beam geometry. The MCG scheme is evaluated using the proposed FEM framework.","abstract_has_math":false,"creators":["Campos, Rafael Saraiva"],"institution":"Universidade Federal do Rio de Janeiro","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Campos, Marcello Luiz Rodrigues de"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-06","date_published":"2017-06","updated_at":"2026-07-24T01:16:18Z","subjects":["Mapas especiais","Ambientes fechados","Algoritmos","Método dos elementos finitos"],"languages":["eng"],"rights":["Acesso Aberto"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11422/6081","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Campos, Marcello Luiz Rodrigues de"]},{"key":"dc:creator","label":"Author","values":["Campos, Rafael Saraiva"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-01-10T16:20:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-16T03:05:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-06"]},{"key":"dc:publisher","label":"Institution","values":["Universidade Federal do Rio de Janeiro"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia"]},{"key":"dc:type","label":"Dc Type","values":["Tese"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mapas especiais","Ambientes fechados","Algoritmos","Método dos elementos finitos"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Acesso Aberto"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11422/6081"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The first part of this work introduces a novel machine-learning based location engine aiming at improving floor identification accuracy in radio frequency (RF) multi-floor indoor positioning while preserving a low two-dimensional positioning error. The location engine also reduces the position fix computational complexity. A large database of RF samples was collected in a 13-storey building to evaluate the proposal. The second part of this work studies in detail radio tomographic imaging, also referred to as RF-based through-the-wall mapping (TWM). Four different reconstruction algorithms – two projective and two algebraic – are compared using a path-loss model corrupted by Rayleigh noise in a parallel-beam acquisition geometry. After that, the thesis proposes applying the Finite Element Method (FEM) to simulate several parallel-beam geometry RF TWM setups, providing a more accurate simulation model. The meshing parameters of the FEM model geometry have been optimized, enabling a significant computational cost reduction while preserving accuracy. Reconstruction of two floor maps is carried out using the FEM model with different sampling rates, operational frequencies, and antenna models. Finally, a multi-sensor circular acquisition geometry (MCG) is defined to reduce the time required to acquire the RF samples in comparison to the parallel-beam geometry. The MCG scheme is evaluated using the proposed FEM framework."]},{"key":"dc:title","label":"Title","values":["Contributions to radio frequency indoorpositioning and through-the-wall mapping"]}]}],"canonical_facts":{"dc:contributor.advisor":["Campos, Marcello Luiz Rodrigues de"],"dc:creator":["Campos, Rafael Saraiva"],"dc:date.accessioned":["2019-01-10T16:20:43Z"],"dc:date.available":["2026-05-16T03:05:26Z"],"dc:date.issued":["2017-06"],"dc:description.abstract":["The first part of this work introduces a novel machine-learning based location engine aiming at improving floor identification accuracy in radio frequency (RF) multi-floor indoor positioning while preserving a low two-dimensional positioning error. The location engine also reduces the position fix computational complexity. A large database of RF samples was collected in a 13-storey building to evaluate the proposal. The second part of this work studies in detail radio tomographic imaging, also referred to as RF-based through-the-wall mapping (TWM). Four different reconstruction algorithms – two projective and two algebraic – are compared using a path-loss model corrupted by Rayleigh noise in a parallel-beam acquisition geometry. After that, the thesis proposes applying the Finite Element Method (FEM) to simulate several parallel-beam geometry RF TWM setups, providing a more accurate simulation model. The meshing parameters of the FEM model geometry have been optimized, enabling a significant computational cost reduction while preserving accuracy. Reconstruction of two floor maps is carried out using the FEM model with different sampling rates, operational frequencies, and antenna models. Finally, a multi-sensor circular acquisition geometry (MCG) is defined to reduce the time required to acquire the RF samples in comparison to the parallel-beam geometry. The MCG scheme is evaluated using the proposed FEM framework."],"dc:identifier.uri":["http://hdl.handle.net/11422/6081"],"dc:language":["eng"],"dc:publisher":["Universidade Federal do Rio de Janeiro"],"dc:publisher.department":["Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia"],"dc:rights":["Acesso Aberto"],"dc:subject":["Mapas especiais","Ambientes fechados","Algoritmos","Método dos elementos finitos"],"dc:title":["Contributions to radio frequency indoorpositioning and through-the-wall mapping"],"dc:type":["Tese"]},"updated_at":"2026-07-24T01:16:18Z"}