Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 64, Issue 4, Pages 1263-1278Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2015.2403868
Keywords
Bayesian filtering; data fusion; indoor tracking; simultaneous localization and mapping (SLAM); technologies for tracking
Categories
Funding
- Spanish Ministry of Economy and Competitiveness [C2012-39143]
- European Commission in the Network of Excellence in Wireless COMmunications (NEWCOM) [318306]
- Generalitat de Catalunya [2014-SGR-1567]
- National Science Foundation [CCF-1320626, ECCS-1346854, CNS-1354614]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1346854] Funding Source: National Science Foundation
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In the last decade, the research on and the technology for outdoor tracking have seen an explosion of advances. It is expected that in the near future, we will witness similar trends for indoor scenarios where people spend more than 70% of their lives. The rationale for this is that there is a need for reliable and high-definition real-time tracking systems that have the ability to operate in indoor environments, thus complementing those based on satellite technologies, such as the Global Positioning System (GPS). The indoor environments are very challenging, and as a result, a large variety of technologies have been proposed for coping with them, but no legacy solution has emerged. This paper presents a survey on indoor wireless tracking of mobile nodes from a signal processing perspective. It can be argued that the indoor tracking problem is more challenging than the problem on indoor localization. The reason is simple: From a set of measurements, one has to estimate not one location but a series of correlated locations of a mobile node. The paper illustrates the theory, the main tools, and the most promising technologies for indoor tracking. New directions of research are also discussed.
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