For Lidar technology that can identify the location of the target substances and measure spatial distribution, the establishment of that technology is required so that it can comprehensively provide remote measuring hazardous substances that cause harm to human bodies, such as toxic substances and combustible substances. Hazardous substances exist in a very wide range of forms, for example, chemical species, physical conditions, and organisms or inorganisms. In addition, substances developed for the purpose of attacking the human body, represented by nerve agents, exhibit their effects by a small amount. Therefore, in order to realize remote sensing of hazardous substances, it is necessary to apply an excellent measurement principle that can respond to the diversity and the detection of trace components of these objects. The Raman effect is a useful phenomenon that enables identification of many individual substances, but the extremely weak response has led to significant limitations in applicable fields. In this study, we conducted basic experiments for the realization of remote sensing technology of hazardous substances based on the resonance Raman effect. The resonance Raman effect is a phenomenon in which the intensity of Raman scattering light is greatly enhanced by excitation with light of a wavelength corresponding to the electronic transition energy of the target substance. The presence of electronic transition energy of substances can be confirmed by observing the ultraviolet absorption spectra. Many hazardous substances exhibit ultraviolet absorption in the deep ultraviolet wavelength region of 300 nm or less. Therefore, in this study, we constructed a resonance Raman spectrum measuring device capable of wavelength sweeping in the deep ultraviolet wavelength range, selected SO<sub>2</sub> and NH<sub>3</sub>, typical corrosive gases, as target substance, and verified experimentally the enhancement of Raman signal intensity by resonance Raman effect.
As a fundamental study for improving the detection accuracy of Raman spectroscopy under noisy conditions, this paper proposes a novel spectrum decomposition method, where the observed spectrum from an unknown substance is decomposed into some known spectra. Raman spectroscopy can be used for a remote sensing method, where a laser is irradiated to the target and then the Raman scattering light is analyzed to detect the target constituents. The spectrum decomposition is the method to analyze the observed spectrum, that is the Raman scattering light, with some known spectra, which are previously developed as a database. The purpose of the decomposition is to find a linear combination of the known spectra so that the linear combination appropriately represents the observed spectrum. The coefficients of the linear combination show the density of molecules contained in the target. The coefficients can be found with multiple linear regression method. However, the coefficients can contain large errors under low signal-noise-ratio conditions. The proposed method tries to overcome the noise problem by using three techniques. The first technique is to employ the nonnegative least squares method, which is the least squares method with non-negative constraints for the coefficients. The second technique is to select the wavelengths of the observed and known spectra for the spectrum decomposition. The third technique is to select the wavelength of the laser irradiated to the target. This paper conducts numerical experiments to show the effectiveness of the proposed method.