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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Localization of robots is vital for navigation and path planning, such as incases where a map of the environment is needed. Ultra-Wideband (UWB) for indoorlocation systems has been gaining popularity over the years with theintroduction of low-cost UWB modules providing centimetre-level accuracy.However, in the presence of obstacles in the environment, Non-Line-Of-Sight(NLOS) measurements from the UWB will produce inaccurate results. As low-costUWB devices do not provide channel information, we propose an approach todecide if a measurement is within Line-Of-Sight (LOS) or not by using somesignal strength information provided by low-cost UWB modules through a NeuralNetwork (NN) model. The result of this model is the probability of a rangingmeasurement being LOS which was used for localization through theWeighted-Least-Square (WLS) method. Our approach improves localization accuracyby 16.93% on the lobby testing data and 27.97% on the corridor testing datausing the NN model trained with all extracted inputs from the office trainingdata.

NLOS Ranging Mitigation with Neural Network Model for UWB Localization