Heterogeneous Feature Machine Learning for Performance-Enhancing Indoor Localization

Currently there is a trend in indoor localization by utilizing machine learning. However, the precision and robustness are limited due to single feature machine learning scheme. The reason behind is that single feature cannot capture the complete channel characteristics and susceptible to interference. The objective of this paper is to introduce heterogeneous features fusion model to enhance the precision and robustness of indoor positioning. Its effectiveness and efficiency are proved by comparing with current benchmark.