Naghdi, ShararehO'Keefe, Kyle2022-06-092022-06-092022-06-07Naghdi, S., & O’Keefe, K. (2022). Combining multichannel RSSI and vision with artificial neural networks to improve BLE trilateration. Sensors, 22(4320). https://doi.org/10.3390/s221243201424-8220http://hdl.handle.net/1880/114715https://dx.doi.org/10.11575/PRISM/39813The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.engUnless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.advertising channelstrilaterationBLEartificial intelligencelocalizationhuman body shadowingjournal articleCRDPJ 514520–1710.3390/s22124320