  For signal processing related to localization technologies, non line of sight
(NLOS) multipaths have a significant impact on the localization error level.
This study proposes a localization correction method based on convolution
neural network (CNN), which extracts obstacle features from maps to predict the
localization errors caused by NLOS effects. A novel compensation scheme is
developed and structured around the localization error in terms of distance and
azimuth angle predicted by the CNN. Four prediction tasks are executed over
different building distributions within the maps for typical urban scenario,
resulting in CNN models with high prediction accuracy. Finally, a thorough
comparison of the accuracy performance between the time difference of arrival
(TDOA) localization algorithm and the results after the error compensation
reveals that, generally, the CNN prediction approach demonstrates great
localization error correction performance, improving TDOA accuracy by 75%. It
can be observed that the powerful feature extraction capability of CNN can be
exploited by processing surrounding maps to predict the localization error
distribution, showing great potential for further enhancement of TDOA
performance under challenging scenarios with rich multipath propagation.
