Compressive Parameter Estimation for Correlated Frames in MIMO Visible Light Communications

Abstract

Estimation of the image location of a mobile LED array transmitter on the camera sensor plane of a digital camera is considered. A Bayesian framework is used to design an initial large measurement matrix over multiple image frames to exploit inter-frame temporal correlations. This large matrix is then factorized into two component matrices; one is used as the front-end measurement matrix with the potential for hardware implementation while the other is designed for software implementation. The factorization can be performed offline through fixed point iterations. The proposed measurement matrix design directly exploits possible transmitter mobility through a parametric framework. The findings are validated through analysis, computer simulation and hardware experiments. Sub-pixel localization accuracy with significant compression is achieved, and the proposed compressive parameter estimation outperforms results obtained using a Gaussian measurement matrix.