Indoor localization with channel impulse response based fingerprint and nonparametric regression

In this paper, we propose a fingerprint-based localization scheme that exploits the location dependency of the channel impulse response (CIR). We approximate the CIR by applying inverse Fourier transform to the receiver's channel estimation. The amplitudes of the approximated CIR (ACIR) vector are further transformed into the logarithmic scale to ensure that elements in the ACIR vector contribute fairly to the location estimation, which is accomplished through nonparametric kernel regression. As shown in our simulations, when both the number of access points and density of training locations are the same, our proposed scheme displays significant advantages in localization accuracy, compared to other fingerprint-based methods found in the literature. Moreover, absolute localization accuracy of the proposed scheme is shown to be resilient to the real time environmental changes caused by human bodies with random positions and orientations.