Equality Constrained-Optimization-Based Semi-supervised ELM for Modeling Signal Strength Temporal Variation in Indoor Location Estimation

Signal strength can be used to estimate location of a wireless device. As compared to other signal measures such as time-based and angle-based metrics, signal strength is normally embedded in wireless transceivers. This allows us to add location estimation feature on top of any wireless systems without requiring hardware modification. However, signal strength is affected by many environmental factors which cause temporal and spatial variation that could degrade the accuracy of location estimation system if not handled properly. In this paper, we focus on the temporal variation effect which is inevitable in dynamic environments where people and surrounding objects are typically not stationary. We try to improve the Location Estimation using Model Trees (LEMT) algorithm, a previous work that uses M5 model tree, by proposing that the calibration of the radio map over time can be done using Equality Constrained-optimization-based Semi-Supervised Extreme Learning Machine (ECSS-ELM). By using continuous signal strength readings collected from reference tags and tracking tag of a 2.4-GHz Radio Frequency Identification (RFID) system, we found that the algorithm can achieve comparable performance with much faster training time and testing time as compared to the M5 model tree.