Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization
This work addresses efficiency and adaptability issues in fingerprinting localization for indoor positioning systems, but it is incremental as it applies existing deep learning techniques to a specific domain.
The paper tackles the cumbersome training database construction and extrapolation of fingerprinting algorithms for similar buildings in RSSI-based fingerprinting localization by using data augmentation and transfer learning, resulting in effectively reduced training data and successful model transfer with significantly smaller training numbers.
Considered as a data-driven approach, Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. This papers addresses applications of artificial intelligence to solve two problems in Received Signal Strength Indicator (RSSI) based FPS, first the cumbersome training database construction and second the extrapolation of fingerprinting algorithm for similar buildings with slight environmental changes. After a concise overview of deep learning design techniques, two main techniques widely used in deep learning are exploited for the above mentioned issues namely data augmentation and transfer learning. We train a multi-layer neural network that learns the mapping from the observations to the locations. A data augmentation method is proposed to increase the training database size based on the structure of RSSI measurements and hence reducing effectively the amount of training data. Then it is shown experimentally how a model trained for a particular building can be transferred to a similar one by fine tuning with significantly smaller training numbers. The paper implicitly discusses the new guidelines to consider about deep learning designs when they are employed in a new application context.