低軌道立方衛星（LEO）因其低延遲、高覆蓋率與靈活部署的特性，已成為現代通訊與訊號收集的重要平台。然而，在低軌道衛星收集未知訊號源的語音訊號（如AM或FM訊號）時，常面臨低訊雜比（SNR）以及未知訊號載波頻率等挑戰，導致語音訊號的品質下降，嚴重影響訊號的可理解性與後續應用。本研究旨在開發一種針對低軌道衛星收集的低訊雜比、未知訊號源語音訊號的解調與增強方法，整合小波分解與主成分分析進行降噪。透過結合這些技術，本研究期望克服未知訊號源與低訊雜比環境的限制，提升接收語音訊號的品質，為利用低軌道衛星進行情報收集提供高效、可靠的訊號處理解決方案。

Low Earth Orbit (LEO) CubeSats have become critical platforms for modern communications and signal collection due to their low latency, wide coverage, and flexible deployment. However, when collecting voice signals (such as AM or FM signals) from unknown sources, LEO satellites often face challenges like low signal-to-noise ratio (SNR) and unknown carrier frequencies, which degrade signal quality and significantly impact intelligibility and subsequent applications. This thesis aims to develop a demodulation and enhancement method for low SNR voice signals from unknown sources collected by LEO satellites, integrating wavelet decomposition and principal component analysis (PCA) for noise reduction. By combining these techniques, this research seeks to overcome the limitations of unknown signal sources and low SNR environments, improving the quality of received voice signals and providing efficient, reliable signal processing solutions for intelligence gathering using LEO satellites.

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