The signal-receiving end of acquisition, pointing, and tracking (APT) systems applied to intersatellite laser communication terminals usually uses a fast-steering mirror (FSM) to control the fiber-coupling process, has a complex structural design, and induces large errors in the nonideal coaxial optical path. Herein, we propose a fiber-optic nutator using a piezoelectric ceramic tube (PCT) as the driving unit that allows scanning in the focal plane of the light signal to achieve active fiber coupling in the APT system. Specifically, this article describes the structural design principle of a PCT-based fiber optic nutator, establishes a simulation model of the mechanism, and proves the correctness of the simulation model by measuring the deflection angle of a PCT based on a parallel light collimator. The minimum accuracy of the designed nutator was 0.145 μm, the maximum nutation radius R was 20.09 μm, and the maximum nutation bandwidth was 20 kHz, as determined through simulation. Finally, the design parameters of the nutator were evaluated. The PCT-based fiber optic nutator, which met the design parameters, structurally replaced the fiber optic coupling component FSM and fine tracking camera in conventional APT systems successfully. Therefore, the PCT-based fiber optic nutator allows the active coupling control of signal light to a single-mode fiber (SMF) based on energy feedback on a theoretical basis and promotes the lightweight design of relay optical paths in APT systems. In addition, with future work in optimization of the nutation control algorithm, the scanning range and accuracy of the nutator can be improved.

Laser pulses experience significant temporal broadening in underwater environments due to strong turbulence and scattering effects. As water turbidity increases, the likelihood of multiple scattering events rises, further intensifying pulse broadening and thereby degrading the ranging accuracy of underwater single-photon LiDAR systems. Accurate characterization of the return pulse shape is crucial for precise distance extraction, typically achieved via cross-correlation with the system’s Instrument Response Function (IRF). Conventional models often fail to accurately characterize the distinctive asymmetric shape of underwater LiDAR returns, which feature a rapid rise and a slow decay. To address this limitation, this paper proposes a Modified Biexponential Distribution (MBD) model, specifically designed to capture both the sharp leading edge and the gradual trailing decay of the pulses. This model enables a more accurate representation of the broadened pulse, effectively mitigating the ranging error induced by scattering. Experimental validation demonstrates that, at an attenuation length of 6.9, the Depth Absolute Error (DAE) is reduced from 3.82 cm to 3.15 cm (a 17.54% improvement), while the probability of achieving a DAE below 3.82 cm increases from 49.70% to 74.83%. These results confirm the effectiveness and robustness of the proposed model in enhancing the ranging accuracy of underwater photon-counting LiDAR systems. Furthermore, this study provides a model-driven analytical basis for improving underwater photon detection and bathymetric performance in turbid conditions.

In light of recent advancements in synchrotron radiation technology and nano-technology, there has been a marked increase in the need for ultra-precision nano-positioning mechanisms. This paper presents a method that integrates physical models with multi-objective optimization for developing an optical angle nano-positioning mechanism. We begin by examining the actual motion law of the mechanism, based on kinematic principles. The outcomes from this kinematic analysis facilitate a static analysis of the flexible hinge, identified as a critical component of the mechanism. Subsequently, we establish a dynamic model for the entire mechanism. By employing the physical model as a base and combining it with the optimization algorithm, we identify the optimal design parameters for the mechanism. The design achieves a resolution of 50 nrad and meets the specified requirements. The first-order inherent frequency of the mechanism is approximately 43.75 Hz. There is a discrepancy of 2.63% from the finite element modal analysis results and a 3.33% difference from the theoretical analysis results, validating the reliability of the design method proposed in this study.

To achieve precise measurement without landing, the high-mobility vehicle-mounted theodolite needs to be leveled quickly with high precision and ensure sufficient support stability before work. After the measurement, it is also necessary to ensure that the high-mobility vehicle-mounted theodolite can be quickly withdrawn. Therefore, this paper proposes a hierarchical automatic leveling strategy and establishes a two-stage electromechanical automatic leveling mechanism model. Using coarse leveling of the first-stage automatic leveling mechanism and fine leveling of the second-stage automatic leveling mechanism, the model realizes high-precision and fast leveling of the vehicle-mounted theodolites. Then, the leveling control method based on repeated positioning is proposed for the first-stage automatic leveling mechanism. To realize the rapid withdrawal for high-mobility vehicle-mounted theodolites, the method ensures the coincidence of spatial movement paths when the structural parts are unfolded and withdrawn. Next, the leg static balance equation is constructed in the leveling state, and the support force detection method is discussed in realizing the stable support for vehicle-mounted theodolites. Furthermore, a mathematical model for “false leg” detection is established furtherly, and a “false leg” detection scheme based on the support force detection method is analyzed to significantly improve the support stability of vehicle-mounted theodolites. Finally, an experimental platform is constructed to perform the performance test for automatic leveling mechanisms. The experimental results show that the leveling accuracy of established two-stage electromechanical automatic leveling mechanism can reach 3.6″, and the leveling time is no more than 2 mins. The maximum support force error of the support force detection method is less than 15%, and the average support force error is less than 10%. In contrast, the maximum support force error of the drive motor torque detection method reaches 80.12%, and its leg support stability is much less than the support force detection method. The model and analysis method proposed in this paper can also be used for vehicle-mounted radar, vehicle-mounted laser measurement devices, vehicle-mounted artillery launchers and other types of vehicle-mounted equipment with high-precision and high-mobility working requirements.

During non-landing measurements of a theodolite, the accuracy of the goniometric readings can be compromised by wobble errors induced by various factors such as wind loads, theodolite driving torque, and the stiffness of the supporting structure. To...

Infrared imaging has unique advantages in remote sensing observation and non-contact measurement, but its inherent low contrast and blurred structural details limit the reliability of scene interpretation by human observers. Unlike deep learning-based approaches that rely on data-driven training and substantial computational resources, we propose a Hybrid Prior Enhanced Decomposition (HPED) model, a training-free, model-driven framework that incorporates structural and luminance priors into a multi-stage enhancement pipeline. An l₁–l₀-regularized decomposition separates the input into a base layer that preserves global structures and salient edges and a detail layer in which low-amplitude fluctuations and noise are suppressed. A prior-preserving bi-gamma correction method enhances base-layer contrast through prior-guided histogram segmentation and adaptive gray-level redistribution. An improved grayscale mapping strategy further enhances global contrast while maintaining interframe consistency. Experiments on real SWIR, MWIR, and LWIR images show that HPED ranks first among evaluated traditional and deep learning-based methods on key perceptual quality metrics (SSIM, VIF, LIF), while achieving over 25 fps on a CPU-only platform, sufficient for smooth real-time visual display. Task-oriented evaluation further shows that the HPED improves CNR and SCR by 174.7 ± 11.4% and 298.5 ± 52.1% on average over the raw input, outperforming all competing methods and suggesting potential applicability in downstream machine perception tasks such as detection and tracking.