## 4 Discussion  
Different from the rotary-wing UAV used in previous studies, we use an unmanned helicopter as the flight platform to carry the FTIR system. The large payload capacity and long endurance of the unmanned helicopter lowers the weight and power consumption requirements of the spectrometer. The physical design of the interference module enhances its stability to adapt it to the vibration environment of the unmanned helicopter.  
The field SF\({}_{6}\) detection experiment results show that the interference fringes collected in the unmanned helicopter before and after takeoff are of the same quality, and spectra  
Figure 11: SF\({}_{6}\) gas feature extraction results. (**a**) The background spectrum is directly subtracted from the target spectrum. (**b**) The low-rank background is estimated and separated based on RPCA algorithm.  
Figure 10: Background spectrum and characteristic spectrum of SF\({}_{6}\) gas. The bold red line is the spectrum of SF\({}_{6}\) gas and the rest are multiple background spectra.  
are accurately measured during the flight. Comparing the gas feature extraction results using the two methods, it can be seen that it is difficult to obtain ideal results by directly subtracting the background spectrum of the surrounding environment from the target spectrum because the pure background spectrum is difficult to measure. The difference between the pure background spectrum and the surrounding environment spectrum causes the results to be disturbed by noise. The RPCA algorithm is able to recover the low-rank background data from arbitrarily large magnitude and sparsely noisy observations and is robust despite noise. The gas feature extraction algorithm based on RPCA shows better results, in which the absorption characteristics are more obvious and there is less noise interference. However, the RPCA method requires the measurement of multiple background spectra, which is difficult for some burst leakage gas measurements. In practical gas leak monitoring applications, we consider establishing a background spectrum library for multiple time periods or collecting multiple spectra around the detection area to solve this problem. If the unmanned helicopter payload is large enough, designing a blackbody calibration light path inside the spectrometer can provide a richer selection of gas absorption feature extraction algorithms.  
The sensitivity of the system is limited by the atmospheric changes in the sensing path and the noise introduced by the system itself. Due to the high wind speed in the experimental environment, the concentration of SF\({}_{6}\) gas cannot be accurately measured. With our current equipment, it is difficult to quantify the interference of atmospheric changes on the detection sensitivity of the system. To measure the detection sensitivity limit of the system itself, spectra of SF\({}_{6}\) and nitrogen gas mixtures with different standard concentrations in the gas pool were collected in the laboratory. A standard blackbody with a difference of 5 K from the environment temperature was used as the background. The measured spectra of pure nitrogen and 10 ppm, 20 ppm, 50 ppm and 100 ppm concentration SF\({}_{6}\) gas are shown in Figure 12.  
The spectra in Figure 12 show a relatively good response of the FTIR system to SF6 gas at concentrations above 50 ppm in the laboratory environment. The results can be used as a reference for the detection performance of the system in the field environment. We plan to carry out more experiments to further study the detection ability of the FTIR system under different atmospheric environment conditions in the future.  
Figure 12: Spectra of pure nitrogen and SF\({}_{6}\) gas with 10 ppm, 20 ppm, 50 ppm and 100 ppm standard concentrations using a blackbody with a difference of 5 K from the environment temperature as the background.