Noise immunity performance and filtering method of Frenet and Bishop frames in multicore fiber shape sensing technology

In this paper, in order to compare the noise-resistant ability of Frenet framework and Bishop framework in multi-core fiber shape sensing technology, and to study the effect of different filtering methods on the improvement of the shape reconstruction accuracy of the two frameworks, the study verifies the feasibility, accuracy and noise-resistant ability of the shape sensing based on the two frameworks respectively by taking the cylindrical helix curve as an example, and investigates the effect of the reconstruction error of the Frenet framework and the Bishop framework based on the different filtering methods respectively. and Bishop framework on the reconstruction error, respectively. The results show that under the ideal state of no noise, both Frenet and Bishop frames can achieve reconstruction errors no higher than 0.13%, while the former's noise immunity is obviously inferior to the latter's. The results show that the Frenet and Bishop frames can achieve reconstruction errors no higher than 0.13% under the ideal state of no noise. Meanwhile, low-pass filtering can most significantly improve the reconstruction accuracy of the Frenet framework, while for the Bishop framework, median filtering is the most effective. The results of this paper provide theoretical support for the optimization of multi-core fiber shape sensing technology, and provide a reference for the selection of frames and the application of filtering methods in different application scenarios.