Research On FBG Signal Compression Transmission Andaddressing Positioning For Large Capacity Sensor Network

With the development of technology,sensors are widely used in the fields of monitoring machinery and equipment,such as rail settlement,marine pipelines and so on.Fiber Bragg grating(FBG)sensor is characterized bynumerous advantages,such as light weight,flexible installation,antielectromagnetic interference,high security,and dynamic distributionnetwork,etc.Therefore,these merits make FBG a good alternativesensing element for applications under extreme environmental conditions.In order to better monitor the health of the system,a large number of sensors are often needed in the sensing network.But high density data leading to challenge for data handle in FBG sensing system.Therefore,it is of great significance and application value to compress and address the data of FBG sensing signals.Aiming at the problem that high density data leading to challenge for data transmission and storage in FBG sensing system,a segmented adaptive sampling compression sensing and Improved Orthogonal Matching Pursuit(SASCS-IOMP)algorithm are proposed.The Gabor filter with specific parameters is designed to extract frequency points of the upper sideband with the largest slope in the FBG spectral signal,and the signal is segmented adaptively according to the position of the initial FBG central wavelength achieved by the Hilbert transform.Different SNR thresholds are set according to the amount of information in each section of the spectrum for reducing the overall compression ratio.To speed up algorithm speed,PID algorithm is introduced to design an adaptive step growth mechanism.Finally,an improved orthogonal matching pursuit algorithm is used to reconstruct the spectrum for decoding.And the reconstructed root mean square error is less than 0.7% within 3dB bandwidth of FBG spectrum.Large-capacity encoding FBG sensor network is widely used in modern long-term healthmonitoring system.Encoding FBG sensors have greatly improved the capacity of distributed FBG sensor network.However,the error of addressing increases correspondingly with the enlarging of capacity.To address the issue,an improved algorithm called genetic tracking algorithm(GTA)is proposed in the paper.In the GTA,for improving the success rate of matching and reducing the large number of redundant matching operations generatedby sequential matching,the individuals are designed based on the feasible matching.Then,two kinds of self-crossover ways and a dynamic variation during mutation process are designed to increase the diversity of individuals and to avoid falling into local optimum.Meanwhile,an assistant decision is proposed to handle theissue that the GTA cannot solve when the variation of sensor information is highly overlapped.The simulationresults indicate that the proposed GTA has higher accuracy compared with the traditional tracking algorithm and the enhanced tracking algorithm.