Optical interferometry has emerged as a cornerstone technology for high-precision length measurement, offering unparalleled accuracy in various scientific and industrial applications. This review provides a comprehensive overview of the latest advancements in optical interferometry, with a focus on grating and laser interferometries. For grating interferometry, systems configurations ranging from single-degree- to multi-degree-of-freedom are introduced. For laser interferometry, different measurement methods are presented and compared according to their respective characteristics, including homodyne, heterodyne, white light interferometry, etc. With the rise of the optical frequency comb, its unique spectral properties have greatly expanded the length measurement capabilities of laser interferometry, achieving an unprecedented leap in both measurement range and accuracy. With regard to discussion on enhancement of measurement precision, special attention is given to periodic nonlinear errors and phase demodulation methods. This review offers insights into current challenges and potential future directions for improving interferometric measurement systems, and also emphasizes the role of innovative technologies in advancing precision metrology technology.

Grating interferometers highly valued for high resolution, straightforward benchmark and insensitivity to environmental disturbances, have become one of the most mainstream methods for ultra-precision positioning. Traditional heterodyne grating interferometers which use a dual-frequency orthogonally polarized laser source suffer nanometer-level periodic nonlinear error due to frequency and polarization mixing, thus becoming incompetent to undertake sub-nanometer measurement. Based on the design of two separated laser beams with a small frequency difference for heterodyne beating, a wavelength-stabilized and quasi-common-path heterodyne grating interferometer is proposed. It incorporates a symmetrical oblique incidence structure where the residual dead path is calibrated and the laser source wavelength is stabilized to the rubidium atomic transition line, diminishing heterodyne phase errors resulted from the optical path difference. This approach not only significantly mitigates periodic nonlinear error to below 0.3 nm, but also enhances measurement resolution, repeatability and interference robustness. The proposed heterodyne grating interferometer is determined for ultra-precision positioning serving for precision machinery manufacturing and scanning beam interference lithography and so forth.

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation.

As a flourishing research topic in the field of remote sensing, spatial compressive imaging (SCI) can utilize prior knowledge to recover high-dimensional signals from low-resolution measurements through joint sampling and compression, thus contributing to the bandwidth reduction of information transmission. However, most of the existing SCI methods based on deep learning cannot effectively utilize prior information, and difficult to perform deep extraction of image features, so the reconstruction is not ideal in the case of low sampling ratio. To address the above difficulty, we propose an SCI network based on meta-attention (MA) and swin transformer, named Meta-TR. We adopt the swin transformer as the network backbone, through the wide application of self-attention mechanisms, to achieve deeper extraction of image features, thereby improving the reconstruction quality under low sampling ratios. In addition, we design an MA module, which adopts Squeeze-Excitation architecture to convert the metadata of SCI image degradation process to attention vectors. Then, the attention vectors are used in the channel modulation of network feature maps to guide the network training. Extensive experiments are performed on different benchmark remote sensing datasets and different sampling ratios to confirm the superiority of the proposed Meta-TR method.

Traditional compressive imaging reconstruction is often based on an iterative approach, which costs much time. To deal with the issue, a couple of groups have used deep learning for reconstruction to ensure low running time with good performance. However, the excessive dependence on data and network structure also creates a network with a lack of flexibility and interpretation. Such networks are often inapplicable when compression ratios are high. In order to solve these issues, we study an end-to-end network Joinput-CiNet (joint input compressive imaging net). We use a tailored encoding module to make the imaging degradation model part of the network input. Then the network can obtain prior knowledge of the imaging system, thereby improving training efficiency and reconstruction performance. With five broadly used image datasets and experimentally collected infrared (IR) measurements, Joinput-CiNet demonstrates superior reconstruction performance at low compression rates such as 1:16 and 1:64 with fast speed compared with other networks.

When large-scale wind power is connected to the grid through overhead transmission lines (OTLs), it will cause the problem of power fluctuation and line transmission capacity exceeding the limit, which seriously endangers the stable operation of power system. In this paper, reasonable wind abandonment is carried out to meet the transmission capacity constraint based on electro-thermal coupling theory. Meanwhile, the output power after wind abandonment is stabilized by hybrid energy storage to further meet the requirements of wind power grid-connected volatility. First, the wind-storage integration model is established. Then, based on the electro-thermal coupling theory, a wind abandonment strategy is established to meet the transmission capacity constraints of the OTLs, which uses real-time meteorological parameters to calculate the standard of wind abandonment. Then, taking the output power after wind abandonment as input, a wind-storage control strategy based on hybrid energy storage is established, which can adjust wind power output to meet the constraints of wind power grid-connected volatility. Finally, case analysis shows that the proposed strategy can not only realize the safe grid-connection of wind power, but also reasonably distribute the hybrid energy storage power instructions.