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In the autonomous navigation of drones, improving positioning accuracy is of significant importance to obtain highly accurate information on flight velocity. Traditional microwave and acoustic velocity measurement methods have the disadvantages of poor precision and susceptibility to interference. In this study, an unmanned aerial vehicle (UAV)-mounted two-dimensional laser Doppler velocimeter was developed and investigated, and a relevant drone flight navigation and positioning experiment was carried out. The UAV-mounted two-dimensional laser Doppler velocimeter (LDV) prototype developed in this study applies a scheme of dual-beam measurement light, sharing a focusing lens group. After process integration, the performance of the prototype was measured. It shows that a velocity measurement effect with a high signal-to-noise ratio can be achieved by using two measurement probe beams within a working distance range of 40 m–60 m. In the flight experiment, the flight trajectory calculated using the LDV-measured velocity data was compared with the global navigation satellite system (GNSS)-recorded trajectory. The result shows that LDV can achieve an odometer accuracy of 4.8‰. This study has validated the feasibility of the laser Doppler velocimeter in drone navigation and positioning, providing a novel method for reliable and high-precision velocity measurement in autonomous drone navigation.

In dual-fiber optical traps, two counter-propagating laser beams emitted from opposing laser fibers trap and manipulate particles. We describe the operation and performance of a dual-fiber optical trap created using spherical-tapered ended fiber pigtailed to 1436 nm laser diodes. Compared with the dual flat ended fiber optical trap, the dual spherical-tapered ended fiber optical trap increased the axial stiffness from 0.44 pN/µm to 0.99 pN/µm, and increased the lateral stiffness from 1.68 pN/µm to 1.76 pN/µm. The dual-fiber optical trap fabricated by spherical-tapered ended fiber enhanced the trapping efficiency of the optical trap. It expanded the application range and reliability of the dual-fiber optical trap. Additionally, we integrated the dual-fiber optical trap into an optical chip, thereby improving the stability of the system.

A cylindrical resonator gyroscope is a kind of Coriolis gyroscope, which measures angular velocity or angle via processing of the standing wave. The symmetry of a cylindrical resonator is destroyed by different degrees of geometric nonuniformity and structural damage in the machining process. The uneven mass distribution caused by the asymmetry of the resonator can be expressed in the form of a Fourier series. The first three harmonics will reduce the anti-interference ability of the resonator to the external vibration, as well as increase the angular random walk and zero-bias drift of the gyroscope. In this paper, the frequency split of different modes caused by the first three harmonic errors and the displacement of the center of the cylindrical resonator bottom plate are obtained by simulation, and the relationship between them is explored. The experimental results on five fused silica cylindrical resonators are consistent with the simulation, confirming the linear relationship between the n = 1 frequency split and second harmonic error. A method for evaluating the first three harmonic errors of fused silica cylindrical resonators is provided.

The cylindrical resonator gyroscope (CRG) is a kind of solid-state gyroscope with a wide application market. The cylindrical resonator is the key component of CRG, whose quality factor and symmetry will directly affect the performance of the gyroscope. Due to the material properties and fabrication limitations, the actual resonator always has some defects. Therefore, frequency trimming, i.e., altering the local mass or stiffness distribution by certain methods, is needed to improve the overall symmetry of the resonator. In this paper, we made further derivation based on the chemical trimming theory proposed by Basarab et al. We built up the relation between the frequency split and the balanced mass to determine the mass to be removed. Chemical trimming experiments were conducted on three cylindrical fused silica resonators. The frequency splits of the three resonators were around 0.05 Hz after chemical trimming. The relation between frequency split and balanced mass established from experimental data was consistent with the theoretical calculation. Therefore, frequency split can be reduced to lower than 0.05 Hz under rigorous theoretical calculation and optimized chemical trimming parameters.

Object detection in remote sensing plays a crucial role in various ground identification tasks. However, due to the limited feature information contained within small targets, which are more susceptible to being buried by complex backgrounds, especially in extreme environments (e.g., low-light, motion-blur scenes). Meanwhile, event cameras offer a unique paradigm with high temporal resolution and wide dynamic range for object detection. These advantages enable event cameras without being limited by the intensity of light, to perform better in challenging conditions compared to traditional cameras. In this work, we introduce the Multi-Vision Transformer (MVT), which comprises three efficiently designed components: the downsampling module, the Channel Spatial Attention (CSA) module, and the Global Spatial Attention (GSA) module. This architecture simultaneously considers short-term and long-term dependencies in semantic information, resulting in improved performance for small object detection. Additionally, we propose Cross Deformable Attention (CDA), which progressively fuses high-level and low-level features instead of considering all scales at each layer, thereby reducing the computational complexity of multi-scale features. Nevertheless, due to the scarcity of event camera remote sensing datasets, we provide the Event Object Detection (EOD) dataset, which is the first dataset that includes various extreme scenarios specifically introduced for remote sensing using event cameras. Moreover, we conducted experiments on the EOD dataset and two typical unmanned aerial vehicle remote sensing datasets (VisDrone2019 and UAVDT Dataset). The comprehensive results demonstrate that the proposed MVT-Net achieves a promising and competitive performance.