Dispersed fringe sensor （DFS） is an important phasing sensor of next-generation optical astronomical telescopes. The measurement errors induced by the measurement noise of three piston estimation methods for the DFS including leastsquared fitting （LSF） method, frequency peak location （FPL） method and main peak position （MPP） method, are analyzed theoretically and validated experimentally in this paper. The experimental results coincide well with the theoretical analyses. The MPP, FPL, LSF are used respectively when the DFS operates with broadband light （central wavelength： 706 nm, bandwidth： 23 nm）. The corresponding root mean square （RMS） value of estimated piston error can be achieved to be 1 nm, 3 nm, 26 nm, respectively. Additionally, the range of DFS with the FPL can be more than 100 μm at the same time. The FPL method can work well both in coarse and fine phasing stages with acceptable accuracy, compared with LSF method and MPP method.
Co-phasing between different sub-apertures is important for sparse optical synthetic aperture telescope systems to achieve high-resolution imaging. For co-phasing detection in such a system, a new aspect of the system＇s far-field interferometry is analysed and used to construct a novel method to detect piston errors. An optical setup is built to demonstrate the efficacy of this method. Experimental results show that the relative differences between measurements by this method and the criterion are less than 4%, and their residual detecting errors are about 0.01 A for different piston errors, which makes the use of co-phasing detection within such a system promising.
Dispersed fringe sensors are a promising approach for sensing the large-scale physical step between adjacent segments with acceptable accuracy.However,the nature of dispersion in a dispersed fringe sensor leads to the ideal dispersed fringe pattern becoming vulnerable to noise,particularly at low light levels.A reliable merit-functionbased algorithm with an active actuation is introduced here.The feasibility of our algorithm is numerically demonstrated,and Monte Carlo experiments for different signal-to-noise ratios are conducted to assess its robustness.The results show that the method is valid even when the signal-to-noise ratio is as low as 1.
