Illumination sensing using sparse modeling

Light emitting diodes (LEDs) are becoming a common ingredient in many modern day lighting systems as they are capable of producing high intensity light across a wide spread of frequencies. To efficiently obtain desired lighting effects, it is important to sense the light received across different target locations and estimate its unknown properties (amplitudes, frequency offsets and phases) to design 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 lighting 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. We show that exploiting this knowledge of sparsity will provide accurate estimates of the desired parameters.