针对光纤布拉格光栅(FBG)传感系统中数据量庞大、不利于数据传输及存储的问题，提出了一种分段自适应采样压缩感知与改进的正交匹配追踪（SASCS-IOMP）算法。利用设计特定参数的Gabor滤波器提取FBG光谱信号上边带斜率最大的频率点，根据Hilbert变换粗定位FBG中心波长位置，并对FBG光谱进行自适应分割。在不同分割区域设置不同的信噪比阈值，以降低光谱信号的总压缩比。在自适应采样过程中，为缩短算法的运行时间，引入比例-积分-微分控制算法，设计一种自适应步长增长机制，最后利用IOMP算法重构光谱。仿真结果表明，在单峰和多峰情况下，SASCS-IOMP算法都能降低总观测值的数目，且FBG光谱3 dB带宽内的重构误差均在0.7%以内。

Aiming at the problem that high density data results in challenge for data transmission and storage in fibber Bragg grating (FBG) sensing system, we propose a segmented adaptive sampling compressed sensing and improved orthogonal matching pursuit (SASCS-IOMP) algorithm. Firstly, we design the Gabor filter with specific parameters to extract frequency points of the upper sideband with the largest slope in the FBG spectral signal, and adaptively segment the FBG spectrum according to the coarse positioning of the FBG central wavelength achieved by the Hilbert transform. Then, we set different signal to noise ratio (SNR) thresholds in different segmented regions to reduce the overall compression ratio. To speed up algorithm speed, we design an adaptive step growth mechanism based on proportional-integral-derivative control algorithm in the process of adaptive sampling. Finally, we use IOMP algorithm to reconstruct the spectrum. The simulation result shows that the SASCS-IOMP algorithm can reduce the total number of observations in both the single-peak and multi-peak spectra. The reconstructed root mean square error is less than 0.7% within 3 dB bandwidth of FBG spectrum.