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- 01HD11J4XGJG65CCE877E9CVM4 classification P1.
- 01HD11J4XGJG65CCE877E9CVM4 date "2023".
- 01HD11J4XGJG65CCE877E9CVM4 language "eng".
- 01HD11J4XGJG65CCE877E9CVM4 type conference.
- 01HD11J4XGJG65CCE877E9CVM4 hasPart 01HD12XAKN4VRS897330JHD80P.pdf.
- 01HD11J4XGJG65CCE877E9CVM4 hasPart 01HD12XJYMWKXNBD4S7XY20VCQ.pdf.
- 01HD11J4XGJG65CCE877E9CVM4 subject "Technology and Engineering".
- 01HD11J4XGJG65CCE877E9CVM4 doi "10.1109/ICL-GNSS57829.2023.10148923".
- 01HD11J4XGJG65CCE877E9CVM4 isbn "9798350323085".
- 01HD11J4XGJG65CCE877E9CVM4 issn "2325-0747".
- 01HD11J4XGJG65CCE877E9CVM4 presentedAt urn:uuid:91bba8e0-3d5b-4910-8a71-9c06953fe664.
- 01HD11J4XGJG65CCE877E9CVM4 abstract "Seamless localization, navigation, and tracking applications can be realized utilizing different sensors and cameras, radio frequency signals such as WiFi, ultra-wideband, and global navigation satellite system, each of which is better suited for different types of environments. As such, awareness of the environment is crucial for the system to efficiently utilize the most relevant resources in each scenario and enable seamless transition between different environments. For example, when vehicles are moving from an open area such as open highway to crowded urban streets, or the opposite, they experience a considerable environment transition, which triggers opportunities for wide-range environment-specific device and algorithm optimization. In this paper, a novel infrastructure-free method utilizing channel state information of ultra-wideband signals and a convolutional neural network is proposed. This method enables a fast detection of the environment type, including crowded urban and open outdoor, reaching a detection latency of only three milliseconds. The experimental data is collected in the real environments of the city of Ghent, Belgium. The test data set, used for numerical performance evaluations, is collected from areas different from those used in the training set. The results show that the proposed method provides an average environment detection accuracy of 90% in the considered test setup.".
- 01HD11J4XGJG65CCE877E9CVM4 author 0163AB60-203D-11E5-A7F6-DF9EB5D1D7B1.
- 01HD11J4XGJG65CCE877E9CVM4 author 16C16F38-E363-11E2-A6ED-42CB10BDE39D.
- 01HD11J4XGJG65CCE877E9CVM4 author F63BAD40-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HD11J4XGJG65CCE877E9CVM4 author F849FE20-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HD11J4XGJG65CCE877E9CVM4 author F8639E8E-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HD11J4XGJG65CCE877E9CVM4 author urn:uuid:4c02659a-73d5-42b2-8c43-44c4928ad459.
- 01HD11J4XGJG65CCE877E9CVM4 author urn:uuid:b91a07f9-b7e9-4a24-9edb-400e23541dc0.
- 01HD11J4XGJG65CCE877E9CVM4 dateCreated "2023-10-18T09:23:41Z".
- 01HD11J4XGJG65CCE877E9CVM4 dateModified "2024-10-29T18:26:26Z".
- 01HD11J4XGJG65CCE877E9CVM4 name "Fast and precise neural network-based environment detection utilizing UWB CSI for seamless localization applications".
- 01HD11J4XGJG65CCE877E9CVM4 pagination urn:uuid:603e4288-d51c-43c3-ac11-bc4dbf74f827.
- 01HD11J4XGJG65CCE877E9CVM4 publisher urn:uuid:bea82011-ddaa-4f88-a738-b28cee569215.
- 01HD11J4XGJG65CCE877E9CVM4 sameAs LU-01HD11J4XGJG65CCE877E9CVM4.
- 01HD11J4XGJG65CCE877E9CVM4 sourceOrganization urn:uuid:0e027b8b-3cab-4342-8428-8c4fca6cf813.
- 01HD11J4XGJG65CCE877E9CVM4 sourceOrganization urn:uuid:81993888-3393-464f-acac-a8b944426ef6.
- 01HD11J4XGJG65CCE877E9CVM4 type P1.