Learning Adaptive Temporal Radio Maps for Signal-Strength-Based Location Estimation

In wireless networks, a client's locations can be estimated using signal strength received from signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when signal-strength values change over time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as LEMT (Location Estimation using Model Trees), to reconstruct a radio map using real-time signal-strength readings received at the reference points. This algorithm can take into account real-time signal-strength values at each time point and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodate the...

• Type: Journal
• Year: 2008
• Where: TMC
• Authors: Jie Yin, Qiang Yang, Lionel M. Ni
• Keywords: Location Estimation, Radio Map, TMC 2008, Wireless Network