FBG interrogation and the benchmark for algorithms in the processing of experimental data

Fiber Bragg grating (FBG) spectrometric interrogation is widely used in strain, temperature, and refractive index sensing, where the acquired FBG sensor reflected signal has the spectrum peak monitored to infer a physical quantity. This monitoring, or peak detection, is subject to noise and different types of distortion, and many applications require the preprocessing of the signals to achieve the required precision and accuracy. This work extends the benchmarks already proposed in the literature with tests using specific experimental data with reduced signal to noise ratio and with systematic distortions in the FBG sensor. The obtained results show that specific conditions such as the distortion in the spectrum of the light reflected by the sensor can lead to poor performance of algorithms that reportedly show acceptable results under more ideal conditions. A metric for the performance evaluation of algorithms using experimental data in FBG interrogation is also presented.