Research on the model of reinforcement learning in fiber optic sensor signal processing

Addressing the issues of low signal-to-noise ratio in complex noise environments, environmental changes, nonlinear interference, and the high complexity of signal processing under multi-source signal fusion, as well as the limited adaptability of traditional methods, this paper proposes a fiber optic sensor signal processing model. This model combines the dynamic optimization capabilities of reinforcement learning with the rapid adaptability of meta-learning, enabling dynamic adjustment of signal processing strategies to accommodate different environments and requirements. This paper defines the environment, state space, action space, and reward mechanism of the fiber optic sensor signal processing model, and dynamically selects the optimal action through the policy network to achieve signal enhancement and noise suppression. In the experimental section, this paper constructs a mixed dataset containing real fiber optic sensor data and simulated noise, and trains and validates the model under different noise levels and signal characteristics. The results show that compared to traditional methods such as Kalman filtering, the proposed model achieves a signal-to-noise ratio (SNR) of 16.8 dB, an improvement of 4.3 dB; the mean squared error (MSE) is reduced by 25%, and the model convergence speed is increased by 35%, significantly outperforming traditional methods. Additionally, the model demonstrates excellent stability and rapid adaptability across different noise levels. In practical fiber optic sensing applications such as industrial temperature monitoring and medical pressure sensing, the model effectively improves signal quality, reduces false alarm rates, and significantly enhances measurement accuracy, offering promising application prospects.