Weighted average indoor positioning algorithm that uses LEDs and image sensors

We propose a weighted average indoor positioning algorithm, which is an improved version of the M.S. Rahman’s algorithm, for the calculation of unknown positions in a visible light communication system consisting of light-emitting diodes (LEDs) and image sensors. The algorithm considers the LED illumination intensity as a key factor, and the generalized Lambert illumination model is adopted to estimate the LED illumination intensity of each pixel in the images obtained at the sensors. The LED illumination intensity is normalized as a weighting factor, following the determination of the center position of the LED image. Simulations showed that the average signal-to-noise ratio in our positioning system was 19.3 dB. The simulation results also showed that the root mean square positioning error was reduced from 6.6 to 3.7 cm when the resolution of the image sensor was 3000 pixels per cm, which is comparable to the error in the widely used M.S. Rahman’s algorithm. The distance between the centers of the lenses and the focal lengths of the lenses also affects the positioning error. After the simulations, the relationship between the positioning error and the lens distance or focal length is deduced. It is observed that this algorithm has lesser positioning errors than the M.S. Rahman’s algorithm.