盲信号分离与传感器技术融合方法及应用研究
Research on the Fusion Method and Application of Blind Signal Separation and Sensor Technology

本研究旨在探索盲信号分离(BSS)与传感器技术的协同优化机制及其在复杂环境中的应用价值。通过系统梳理BSS核心算法(如ICA、PCA)的数学原理及传感器技术的设计原则(微型化、低功耗、高灵敏度)，提出数据预处理与BSS联合优化策略，以解决多源信号干扰与噪声抑制难题。结合农业传感器网络、MEMS器件动态监测及远程医疗等场景案例，验证了融合技术在农田环境监测、牲畜行为分析及呼吸信号分离中的有效性。结果表明，BSS可显著提升复杂环境下数据采集的准确性与鲁棒性，而传感器技术的微型化与智能化为边缘计算提供了硬件支持。结论指出，两者的结合不仅推动了精准农业与智能医疗的发展，还为基础设施薄弱地区的实时监测提供了技术路径，未来需进一步优化算法轻量化与低成本传感器研发。
This research aims to explore the collaborative optimization mechanism of Blind Signal Separation (BSS) and sensor technology, as well as its application value in complex environments. By systematically reviewing the mathematical principles of core algorithms in BSS (such as ICA and PCA) and the design principles of sensor technology (miniaturization, low power consumption, and high sensitivity), a joint optimization strategy for data preprocessing and BSS is proposed to address the problems of multi-source signal interference and noise suppression. Combined with case studies in agricultural sensor networks, dynamic monitoring of MEMS devices, and remote medical care, the effectiveness of the integrated technology in monitoring agricultural environments, analyzing livestock behavior, and separating respiratory signals has been verified. The results show that BSS can significantly improve the accuracy and robustness of data acquisition in complex environments, while the miniaturization and intelligence of sensor technology provide hardware support for edge computing. The conclusion indicates that the combination of the two technologies not only promotes the development of precision agriculture and intelligent healthcare, but also provides a technical path for real-time monitoring in areas with weak infrastructure. Future research should further optimize the lightweighting of algorithms and the development of low-cost sensors.