We present a general technique to select optimal filters for a multispectral midwavelength infrared (MWIR) camera to quantify multiple components of a gas mixture. Filter sets are ranked in terms of the ratio of quantity-of-interest variance to noise variance, which is minimized by the optimal filter set. The suitability of this criterion is demonstrated using a numerical experiment in which we estimate the combustion efficiency (CE) of a gas flare from MWIR images of the flare. Synthetic hyperspectral intensity data are generated using the spatially-resolved thermochemical state of a simulated flare-in-crosswind. We then compute multispectral images for commercially-available filter sets and estimate the concentration of key species (CH₄ and CO₂) and temperature along lines-of-sight that correspond to sampled pixels. These distributions are converted to a CE and we compare the accuracy of CE estimates for a filter set to our selection criterion. We find that the selection criterion is a good proxy for CE accuracy. Moreover, the optimized filter set makes intuitive sense: the filters are aligned with the CH₄ and CO₂ absorption bands at 3.4 μm, with an emphasis on CH₄ lines because the CE is highly sensitive to the presence of low concentrations of unburned fuel at low temperature. The selection technique can be applied to any number of quantitative multispectral gas imaging scenarios.

我们提出一种通用技术，为多光谱中波红外（MWIR）相机选择最佳滤光片，以量化混合气体的多种成分。过滤器集按照感兴趣量方差与噪声方差之比进行排序，而最佳过滤器集可将其最小化。使用数值实验证明了该标准的适用性，在该数值实验中，我们根据火炬的MWIR图像估算了火炬的燃烧效率（CE）。合成的高光谱强度数据是使用模拟侧风耀斑的空间分辨热化学状态生成的。然后，我们针对商用滤镜集计算多光谱图像，并估算关键物种（CH ₄和CO ₂）和与所采样像素相对应的沿视线的温度。这些分布被转换为CE，我们将滤波器集的CE估计的准确性与我们的选择标准进行比较。我们发现选择标准可以很好地代表CE准确性。此外，优化的滤清器组具有直观的意义：滤清器与3.4 µm的CH ₄和CO ₂吸收带对齐，并重点关注CH ₄线，因为CE对低浓度未燃烧燃料的存在高度敏感。低温。选择技术可以应用于任何数量的定量多光谱气体成像方案。