Abstract: The static MMS spectral reconstruction process is optimized using algorithmic methods. Because the static MMS encodes spectral information across the detector plane in a highly non-local way, optical errors have a non-local effect on the reconstruction which introduces noise and errors at regions throughout the spectral range. Mathematical signal processing techniques are used to condition and deconvolve the spectral image to compensate for non-ideal system behavior. Spectral signal-to-noise and accuracy are both improved, while the inherent resolution and etendue advantages of the static MMS technique are retained.

Abstract: The static MMS spectral reconstruction process is optimized using algorithmic methods. Because the static MMS encodes spectral information across the detector plane in a highly non-local way, optical errors have a non-local effect on the reconstruction which introduces noise and errors at regions throughout the spectral range. Mathematical signal processing techniques are used to condition and de-convolve the spectral image to compensate for non-ideal system behavior. Spectral signal-to-noise and accuracy are both improved, while the inherent resolution and etendue advantages of the static MMS technique are retained.