Map and Localization error prediction dataset

This dataset is derived from a research paper proposing a wireless localization correction methodology based on Convolutional Neural Networks (CNN). The approach involves feature extraction from maps that depict both line of sight (LOS) and non-line of sight (NLOS) effects. The research includes four prediction tasks, categorizing CNN models based on building distribution and propagation mode, resulting in models with low prediction loss. Additionally, an error compensation scheme is designed using CNN-predicted localization errors. Comprehensive comparisons are made between the accuracy of the Time Difference of Arrival (TDOA) wireless localization algorithm and the TDOA results after error compensation. Overall, the CNN prediction method demonstrates significant performance in correcting localization errors. This dataset highlights the importance of preprocessing environmental maps to extract features related to localization error distribution using CNN, especially in complex scenarios involving multipath propagation.