Compressed LED Illumination Sensing

Modern day light emitting diodes (LEDs) are capable of producing high intensity light across a wide spread of frequencies. Hence, they are becoming a common ingredient in many lighting systems. In order to obtain desired lighting effects efficiently, it is important to sense the light received across different target locations and estimate the unknown properties (amplitudes, frequency offsets and phases) of the modulating signals. This facilitates the design of the driving waveforms for the LEDs. This procedure is known as illumination sensing and it enables efficient and effective usage of light energy to achieve the intended effects. We propose a novel two step approach to perform this estimation using sparse modeling which exploits the fact that the measurements at the sensors are sparse in the frequency offset space and the phase space. Further, we employ compressive sensing to reduce the dimensions of the measurement vector, thereby reducing the complexity of the estimation algorithm. This will enable quick estimation which is essential to avoid any lag in attaining the desired illumination effects. Also, we demonstrate the performance of the proposed approach in the presence of a modeling mismatch.