Yue Sun

This paper demonstrates the designs and implements of an FPGA-based LiDAR target simulator (LTS) for pulsed-coded LiDAR testing. The simulator employs a hybrid delay architecture integrating multi-phase clock sampling and FIFO buffering. By combining coarse and fine timing modules, the system achieves a temporal resolution of 0.5 ns, corresponding to a spatial resolution of 7.5 cm. It also supports an ultra-long maximum delay time up to 83.886 ms (12582 km range), covering simulation scenarios involving long-range targets. In addition, the system supports pulse width modulation, allowing it to simulate the temporal broadening of laser pulses caused by environmental factors such as atmospheric turbulence and target scattering, with resolution of 0.5 ns. Experimental results demonstrate that the proposed approach effectively overcomes the limitations of traditional methods in terms of precision, range, stability, and compatibility with various coding schemes. This work provides a reliable technical foundation for the development and evaluation of pulse-coded laser ranging systems.

In this paper, we design and demonstrate an all-fiber-sensitive refractive index (RI) sensor based on the Mach–Zehnder interferometer (MZI). It is constructed by splicing two no-core fibers (NCFs) and a photonic crystal fiber (PCF) between two single-mode fibers (SMFs) to obtain an SMF–NCF–PCF–NCF–SMF composite structure (SNPNS). A study of the effect of varying PCF lengths on the RI reveals that the shorter the length, the higher the sensitivity. The maximum RI sensitivity of 176.9 nm/RIU is attained within the RI range of 1.3365–1.3767 when the PCF length in the SNPNS structure is 3 cm. Meanwhile, the sensor exhibits a high stability in water, with an RSD of only 0.0019% for the interference trough over a duration of two hours. This proposed sensing structure offers the advantages of a large extinction ratio, small size, low temperature sensitivity, and simple fabrication, exhibiting a great potential in RI measurements.

A novel fiber sensor for the refractive index sensing of seawater based on a Mach–Zehnder interferometer has been demonstrated. The sensor consisted of a single-mode fiber (SMF)–no-core fiber (NCF)–single-mode fiber structure (shortened to an SNS structure) with a large lateral offset spliced between the two sections of a multimode fiber (MMF). Optimization studies of the multimode fiber length, offset SNS length, and vertical axial offset distance were performed to improve the coupling efficiency of interference light and achieve the best extinction ratio. In the experiment, a large lateral offset sensor was prepared to detect the refractive index of various ratios of saltwater, which were used to simulate seawater environments. The sensor’s sensitivity was up to −13,703.63 nm/RIU and −13,160 nm/RIU in the refractive index range of 1.3370 to 1.3410 based on the shift of the interference spectrum. Moreover, the sensor showed a good linear response and high stability, with an RSD of only 0.0089% for the trough of the interference in air over 1 h.

Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function F is formed by three objective functions: correlation coefficient R, regression stability coefficient S and mean fit deviation D. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions R, S and D are pre-trained by NRS, and IGWO is used to optimize the fitness function F. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II.

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively.

This article presented an investigation of nonlinear temperature sensing based on soliton self-frequency shift (SSFS) in an in-house fabricated microstructured optical fiber (MOF), and the sensing performance was evaluated by detecting the peak wavelength shift of soliton with the variation of temperature. Both theoretical simulation and experimental research were carried out and there was a good correspondence between the two results. The experimental sensitivity was as high as 0.451 nm/°C at 400 mW with a resolution of 0.2653 °C. Our work is a proof of concept of the optical fiber nonlinear phenomenon of SSFS to sensing technology. This temperature sensor does not involve additional fiber modification, and it has potential applications in biomedical, security, and harsh industrial environment.

According to Faraday rotation effect and Fresnel reflection theory, a novel magnetic field intensity sensor is theoretically proposed and experimentally demonstrated by introducing a 6.4-cm-long tellurite (76.5TeO ₂ -6BiO ₃ -11.5Li ₂ O-6ZnO) no core fiber (TBLZ NCF) into the loop of Sagnac interferometer. The large Verdet constant of TBLZ NCF will bring about an apparent Faraday rotation angle in magnetic field sensing, and the large refractive index difference between TBLZ and silica fibers makes the Fresnel reflection more obvious. The designed sensor demonstrates a sensitivity of 13.51 pm/Gs with an accuracy of 1.47 Gs in the measuring range of 0-380 Gs. It has satisfactory linearity, repeatability, and stability, and does not require additional utilization of expensive magnetic field sensitive materials, exhibiting great application potential.

Based on particle image velocimetry (PIV) technology, the process and flow field characteristics of slag entrapment under the condition of critical slag entrapment are investigated by water model. The change of interface flow field at the slag entrapment process from the occurrence to end of critical slag entrapment is analysed, and the influence of kinematic viscosity of slag on critical slag entrapment velocity is quantitatively analysed. The results show that the kinematic viscosity of slag has an important influence on slag entrapment. The critical blowing flow and critical speed increase with the increasing of the viscosity. Based on the law of conservation of energy and the traditional theoretical calculation models, an improved formula for critical slag entrapment speed is proposed in this study, which further considers the thickness and viscosity of the slag.