1 resultado para engine noise estimation
em CORA - Cork Open Research Archive - University College Cork - Ireland
Filtro por publicador
- KUPS-Datenbank - Universität zu Köln - Kölner UniversitätsPublikationsServer (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (2)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Aston University Research Archive (18)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (77)
- Biblioteca Virtual del Sistema Sanitario Público de Andalucía (BV-SSPA), Junta de Andalucía. Consejería de Salud y Bienestar Social, Spain (2)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (6)
- Brock University, Canada (5)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CentAUR: Central Archive University of Reading - UK (10)
- CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal (5)
- Cochin University of Science & Technology (CUSAT), India (20)
- Collection Of Biostatistics Research Archive (1)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (217)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Dalarna University College Electronic Archive (2)
- Digital Commons - Michigan Tech (5)
- Digital Commons at Florida International University (1)
- Diposit Digital de la UB - Universidade de Barcelona (15)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (69)
- DRUM (Digital Repository at the University of Maryland) (3)
- Duke University (1)
- FUNDAJ - Fundação Joaquim Nabuco (2)
- Gallica, Bibliotheque Numerique - Bibliothèque nationale de France (French National Library) (BnF), France (3)
- Instituto Politécnico do Porto, Portugal (21)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (15)
- Martin Luther Universitat Halle Wittenberg, Germany (7)
- Massachusetts Institute of Technology (15)
- Ministerio de Cultura, Spain (3)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (2)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (1)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (22)
- Repositório da Produção Científica e Intelectual da Unicamp (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (7)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (30)
- School of Medicine, Washington University, United States (3)
- Scielo Saúde Pública - SP (56)
- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (4)
- Universidad Autónoma de Nuevo León, Mexico (2)
- Universidad de Alicante (2)
- Universidad del Rosario, Colombia (2)
- Universidad Politécnica de Madrid (5)
- Universidade Complutense de Madrid (1)
- Universidade do Minho (19)
- Universidade dos Açores - Portugal (1)
- Universidade Federal do Pará (1)
- Universidade Técnica de Lisboa (1)
- Universita di Parma (1)
- Universitat de Girona, Spain (13)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (10)
- Université de Lausanne, Switzerland (136)
- Université de Montréal (1)
- Université de Montréal, Canada (70)
- Université Laval Mémoires et thèses électroniques (1)
- University of Michigan (4)
- University of Queensland eSpace - Australia (62)
Resumo:
The position of a stationary target can be determined using triangulation in combination with time of arrival measurements at several sensors. In urban environments, none-line-of-sight (NLOS) propagation leads to biased time estimation and thus to inaccurate position estimates. Here, a semi-parametric approach is proposed to mitigate the effects of NLOS propagation. The degree of contamination by NLOS components in the observations, which result in asymmetric noise statistics, is determined and incorporated into the estimator. The proposed method is adequate for environments where the NLOS error plays a dominant role and outperforms previous approaches that assume a symmetric noise statistic.