[P-CG24] 宇宙・惑星探査の将来計画および関連する機器開発の展望

[PCG24-18] 独立成分分析を用いた衛星磁場データからのノイズ分離：マスト・ノイズ源情報なし観測への展望

*今城 峻¹、能勢 正仁¹、相田 真里²、松本 晴久²、東尾 奈々²、徳永 旭将⁴、松岡 彩子³ (1.名古屋大学宇宙地球環境研究所、2.宇宙航空研究開発機構、3.九州工業大学、4.JAXA 宇宙科学研究所)

キーワード：衛星磁場観測、独立成分分析、衛星起因ノイズ分離、マストなし磁場観測、統計的信号処理

We propose an application of the independent component analysis (ICA) to separate satellite-induced time-varying noises from magnetic data obtained by onboard multiple magnetometers. The ICA is a method of estimating source signals observed at multiple sites so that estimated source signals are statistically independent of each other. Since satellite noises are clearly independent of external natural variations, the ICA is expected to separate the satellite noises. We applied the ICA to the magnetic data measured by the first Quasi-Zenith Satellite (QZS-1) that has two triaxial fluxgate magnetometers without an extendable mast. First, we removed the long-period trend from the original data. Then, we applied the FastICA to the detrended data and obtained six independent components (ICs). Satellite-induced noises were successfully classified into three ICs (noise ICs). Natural signals are represented by the rest three ICs (signal ICs). Finally, we restored the observed signals from the signal ICs. We confirmed that amplitude and waveform of natural phenomena were not altered by the processing. We also proposed the method of automatic determination of noise ICs using the D score, which is similar to the normalized coefficient of variance of the mixing vectors. We confirmed that the three largest D scores, which give the noise ICs, are much larger than the three smallest D, which give the signal ICs. The automatic determination of noise ICs by this method was 95% identical to that by visual inspection. These results demonstrated that the ICA method can provide for mast-less magnetic observations in future satellite missions.