Ultra-Wideband Indoor Positioning Based on New Fingerprint With Missing CIR Data and Parallel CNN Model

To address the issue of neural network training and prediction failures due to the missing channel impulse response (CIR) data from the anchors under the non-line-of-sight (NLOS) conditions, this letter proposes an Ultra-Wideband (UWB) positioning scheme based on a new position fingerprint and a parallel convolutional neural network (CNN). Firstly, a new position fingerprint composed of multi-channel images is designed in order to avoid the subsequent CNN’s collapse caused by the missing CIR data. Secondly, a parallel CNN is proposed to predict each dimension of position coordinates respectively for an improvement in prediction accuracy. Finally, the Savitzky-Golay filtering is employed to further reduce the mean absolute error (MAE) obtained by the whole proposed scheme. Evaluation is conducted using measured public datasets. Results indicate that, this scheme ensures the model runs stably and reliably even when the CIR data from some anchors are missing. Compared to the traditional single CNN, the MAE of the proposed parallel CNN is reduced by 20.9%. Moreover, the final MAE resulted from the proposed scheme in this letter is 0.173 meters.