Performance Bounds on Passive Indoor Positioning Using Visible Light

In this article, a novel method for passive indoor localization using LED luminaires is proposed where explicit user participation is not required. This approach measures changes in the impulse response between sources and receivers and estimates a location based on optical channel sounding data. An exponential integrating-sphere model is used to represent object impulse response (OIR) from each luminaire source-receiver pair, which is obtained by subtracting impulse response (IR) of the room background (i.e., without an object) from IR when the object is present inside the room. This model is represented as a function of 3D position of the object and depends on both source and receiver parameters and the physical geometry of the room. An analytical expression of the CramrRao lower bound (CRLB) on the proposed passive indoor localization method is derived. The position is also estimated by using a maximum likelihood (ML) estimator which gives the position estimate by maximizing the log-likelihood function of the received noisy OIR waveforms. The results show that the signal-to-noise ratio (SNR) and number of source-receiver pairs used in the estimation, play a crucial role in performance. Typical localization root-mean squared error is less than 10cm over a broad range of light intensities and object locations.