English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this paper, the indoor pedestrian location algorithm is investigated, in which we proposes a novel WiFi/MEMS integration structure for indoor navigation to fuse the information from WiFi and MEMS sensors. In WiFi part, aiming at the problem of large amount of calculation in the algorithm, a partition solution called “moving partition” is originally proposed. The test results from indoor experiments indicate that with “moving partition”, the computation time decreased by almost half and the proposed WiFi/MEMS solution works well, of which the mean positioning accuracy has been greatly improved compared with MEMS alone and WiFi alone. Keywords—indoor navigation, MEMS sensors, WiFi, extended kalman filter

Two-level Indoor Navigation using WiFi and MEMS Sensors