Visible light fingerprint database recovery algorithm based on CP decomposition

Abstract

Visible light communication(VLC) is a new method of indoor communication. It can provide an effective solution for indoor positioning. Fingerprint-based visible light positioning(VLP) has been widely studied for its feasibility and high accuracy. The acquisition of ‘fingerprint database’ is crucial for accurate VLP. However, sparse sensors such as photodiode(PD) can only be arranged because of the space-limited scenario and high costs. Correspondingly, it results in the loss of the fingerprint database. Therefore, it is indispensable to solve the problem of how to effectively and accurately recover the fingerprint database from measurements of sparsely arranged sensors. In this paper, we propose a spatio-temporal constraint tensor completion (SCTC) algorithm based on CANDECOMP/PARAFAC (CP) decomposition to recover the fingerprint database from measurements of sparsely arranged sensors. Specifically, we model the measurements from the spatial and temporal dimensions as a tensor, and formulate the optimization problem based on the low-rank feature of the tensor. To improve the recovery accuracy, spatial and temporal constraint matrices are introduced to effectively constrain the optimization direction when completing the tensor. Spatial constraint matrices are constructed by utilizing the mode-n expansion matrix of the tensor based on the undirected graph theory. Accordingly, the Toeplitz matrix is used as the temporal constraint matrix to excavate the temporal correlation of the tensor. Since the optimization problem is non-convex and difficult to solve, we introduce CP decomposition to decompose the tensor into several factor matrices. By solving the factor matrices, the original tensor is reconstructed. The performance of the proposed SCTC algorithm is confirmed via experimental measured data.