Accuracy improvement of two-dimensional shape reconstruction based on OFDR using first-order differential local filtering.

The accuracy of two-dimensional (2D) shape reconstruction is highly susceptible to fake peaks in the strain distribution measured by optical frequency domain reflectometry (OFDR). In this paper, a post-processing method using first-order differential local filtering is proposed to suppress fake peaks and further improve the accuracy of shape reconstruction. By analyzing the principles of 2D shape reconstruction, an explanation of how fake peaks lead to shape reconstruction errors is provided, along with the introduction of an error evaluation standard. The principle of first-order differential local filtering is presented, and its feasibility is verified by simulation. An OFDR 2D shape reconstruction system is built, with three groups of 2D shape reconstruction experiments carried out, including up bending, down bending and arch bending. The experimental results show that the end errors of the three groups of shape reconstruction are respectively reduced from 2.33%, 2.97%, and 1.07% to 0.25%, 0.78%, and 0.20%, at the shape reconstruction length of 0.5 m. The research demonstrates that the accuracy of OFDR 2D shape reconstruction can be improved by using first-order differential local filtering.

Time-domain slicing optical frequency domain reflectometry.

A time-domain slicing (TDS) optical frequency domain reflectometry is proposed for large strain sensing with better spatial resolution. Compared with the conventional frequency domain slicing (FDS) method, the TDS with a Burg spectrum estimation is capable of enhancing the similarity of a local spectrum under large strain and mostly suppressing the fake peaks during the strain resolving. The experimental results demonstrated that it enables measurements of strain ranging from 600 to 4200 µÎµ with a spatial resolution of 2.4 mm and a narrow optical frequency scanning range of only 10 nm. Moreover, the measurement accuracy is improved by six times by decreasing the root mean square error (RMSE) from 8.6611 to 1.3396 µÎµ without any hardware modification.

Random coding method for SNR enhancement of BOTDR.

A random coding method for a Brillouin optical time domain reflectometer (BOTDR) fiber sensor is proposed. In this method, a series of pulses modulated by random code are injected into the optical fiber to enhance the signal-to-noise ratio (SNR) and further improve the measurement accuracy. Random coding method allows the sensing range to be extended to several tens of kilometers while maintaining meter-scale spatial resolution and lower detection peak power, without modifying the conventional configuration of BOTDR. The decoding principle and the coding gain of random coding method are analyzed and simulated. We experimentally implement the method and evaluate its influence on the performance optimization of BOTDR. Compared with the single pulse with peak power of 10 mW, the measured BFS uncertainty over 4.93 km sensing fiber is reduced from 5.34 MHz to 0.38 MHz when 512-bit random coding pulses with the same peak power are utilized. The experimental results show that the coding gain of 11.93 dB is obtained by 512-bit random coding. Benefitting from the SNR enhancement, the sensing range is extended from 4.93 km to 64.76 km within a root-mean-square error (RMSE) of 3 MHz, when the pulse peak power is only 10 mW and the spatial resolution is 2 m.