基于有限样本的大气偏振模式生成方法

A few-shot learning based generative method for atmospheric polarization modelling

大气偏振模式在导航等领域具有广阔的应用前景,但是由于受到大气偏振信息采集装置物理特性的限制,在同一时刻只能获得局部的、不连续的偏振信息,对实际应用产生影响。针对此问题,本文通过挖掘大气偏振模式分布的连续性,提出一种大气偏振模式生成方法,从局部的偏振信息生成全天域的大气偏振模式。此外,偏振信息往往受到不同的天气条件、地理环境等因素的影响,而这些偏振数据在真实环境中难以采集。针对此问题,本文挖掘不同天气、地理条件下有限样本数据之间的多样性关系,以此关系将生成的大气偏振模式泛化到不同的条件下。本论文在仿真数据和实测数据上进行了实验,并与其它最新方法进行对比,实验结果证明了本文方法的优越性和鲁棒性。

Atmospheric polarization has broad application prospects in navigation and other fields.However,due to the limitation of the physical characteristics of the atmospheric polarization information acquisition device,only local and discontinuous polarization information can be obtained at the same time,which has an impact on the practical application.In order to solve this problem,by mining the continuity of atmospheric polarization mode distribution,this paper proposes a network for generating atmospheric polarization mode from local polarization information.In addition,polarization information is often affected by different weather conditions,geographic environment and other factors, and these polarization data are difficult to collect in the real environment. To solve this problem, this papermines the diversity relationship between the few-shot data under different weather and geographic conditions, bywhich the generated atmospheric polarization mode is generalized to different conditions. In this paper, experimentsare carried out on the simulated data and measured data. Compared with other new methods, the experimental resultsprove the superiority and robustness of this proposed method.