AdaMap: Adaptive radiomap for indoor localization

Abstract

In wireless networks, radiomap (also known as fingerprinting) based locating techniques are commonly used to cope the diverse fading signatures of radio signal, in which probabilistic or static radiomaps are trained in offline phase. A challenging problem of radiomap locating is that the radiomap can be outdated when environments change. Reconstruction of radiomap is time consuming and laborious. In this paper, we exploit the inter-beacon radio signal strength (RSS) to construct adaptive radiomap (AdaMap) by an online self-adjusted linear regression model. The distinct feature of AdaMap is that not only the radio signatures at the training locations vary with the online interbeacon RSS measurements, but also the coefficients of the model are self-adjusted when the environments change significantly, so that AdaMap is highly adaptive to the environment changes. The proposed schemes are evaluated by extensive simulations, with comparisons to the state of art of the radiomap wireless localization methods. The results showed that AdaMap presented dramatical advantages in preserving positioning accuracy when the environments changed over time.