An Indoor NLOS Fingerprint-Based VLP Method Using a Multipixel Photon Counter

Abstract

Visible light positioning (VLP) is a low-cost, highly accurate alternative localization technology for indoor applications that makes use of existing light emitting diode (LED)-based lights, which is highly accurate and low costs. It is, however, a major challenge for the existing VLP systems to achieve line-of-sight (LOS) positioning in complex and variable indoor environments. We propose a non-LOS (NLOS) fingerprint-based VLP system based on a multipixel photon counter (MPPC) to address the problem of obstructed LOS paths. Using MPPC, very faint light can be detected with a very high sensitivity and excellent photon counting capability, which enhances the ability to recognize and detect signals in an NLOS environment. We propose a novel method of generating fingerprint database using the NLOS channel model, which construct the relationship between the received signal strength and the distance from MPPC to the virtual image of LED interpolated by only knowing the distance between the LED and the interpolation position. Furthermore, we propose an optimal parameter weighted K-nearest neighbor algorithm, which utilizes the mean absolute error (MAE) as the evaluation metric. In this algorithm, a grid search method is employed to determine the optimal number of neighbors and the distance metric for each test point, thereby enhancing the positioning accuracy. Using only 25 offline measurements, the measured average positioning error (PE) and 90th percentile error are 4.02 and 9.98 cm, respectively, when the MPPC height is 70 cm.