鉴于获取实测光谱成本高、获取量少和可测量云团种类少等限制因素, 研究污染云团的红外光谱仿真, 对于利用仿真光谱进行光谱识别的算法研究显得至关重要。以往的研究多利用经验的概率模型和经验的或半经验的参数来模拟污染云团的实时扩散, 在此基础上再进行污染云团的红外光谱仿真。文中将利用基于物理的模型来精细地模拟污染云团的扩散, 以弥补概率模型的欠精确性。研究了基于物理的云团扩散的机理, 以及基于此扩散模型上红外光谱的生成方法, 最终将生成的物理模型下的仿真光谱序列、概率模型下的仿真光谱序列和实测的光谱序列进行比较, 得到了更为精准的仿真结果: 就光谱残差而言,最高可提高14%, 并指出了两种模型的适用范围。文中建立的基于物理模型的污染云团扩散及其红外光谱的实时仿真方法, 对于高精度的云团红外光谱仿真及高质量的光谱识别算法研究具有重要意义。

Limited to the high cost, low quantity and few species for the measurement of infrared spectrum of pollutant gas cloud, it is extremely important to simulate infrared spectrum with which to improve spectral identification algorithms. Traditionally, empirical probabilistic model and empirical or semi-empirical parameters were used to simulate the real-time explosion of pollutant gas cloud and furthermore simulate the infrared spectrum. A physical-based model was established, which compensated for the low accuracy of the empirical or semi-empirical models, to finely simulated the explosion of pollutant gas cloud. The mechanism of the physical-based explosion model and the way of infrared spectrum simulation based on the physical-based explosion model were studied. Finally, a comparison between the simulated infrared spectrum based on physical model, simulated infrared spectrum based on probabilistic model and the practical one was made and it got a more accurate result that improved by 14 percent at most as far as spectral residual is concerned and figured out the application scope of two models. The physical-based explosion model and the way of infrared spectrum simulation established are significant to finely simulate infrared spectrum of pollutant gas cloud and study high-quality spectral identification algorithms.