Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-Pérot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term 'frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.

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OPTRA is currently developing a modular, reconfigurable matched spectral filter (RMSF) spectrometer for the monitoring of greenhouse gases. The heart of this spectrometer will be the RMSF core, which is a dispersive spectrometer that images the sample spectrum from 2000 -3333 cm -1 onto a digital micro-mirror device (DMD) such that different columns correspond to different wavebands. By applying masks to this DMD, a matched spectral filter can be applied in hardware. The core can then be paired with different fore-optics or detector modules to achieve active in situ or passive remote detection of the chemicals of interest. This results in a highly flexible system that can address a wide variety of chemicals by updating the DMD masks and a wide variety of applications by swapping out fore-optic and detector modules. In either configuration, the signal on the detector is effectively a dot-product between the applied mask and the sample spectrum that can be used to make detection and quantification determinations. Using this approach significantly reduces the required data bandwidth of the sensor without reducing the information content, therefore making it ideal for remote, unattended systems. This paper will focus on the design of the RMSF core.

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