UWB-based Indoor Localization Using a Hybrid WKNN-LSTM Algorithm

Ultra-wideband (UWB) positioning is widely used in indoor positioning due to its low power consumption and high accuracy. However, owing to UWB clock drift and poor communication conditions, it will cause errors or even loss of UWB signals, which will eventually affect position calculation. Therefore, based on the TDOA algorithm, this paper designs and implements a LSTM-WKNN hybrid positioning model based on UWB. This model uses the LSTM model to predict the TDOA data at the future time, and uses the predicted data to correct the actual UWB measurement data. After modified, the TDOA value is transferred to the WKNN model, then the positioning coordinates are solved. The experimental results show that the average positioning error of this model is within 20cm, and it has high positioning accuracy.