The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m⁻¹ and 2.0 m⁻¹), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization.

Space gravitational wave detection is one of the most critical techniques that maintain high stability of the laser link to reduce the coupling of ranging noise. We model and simulate the laser pointing jitter noise of the laser link between two satellites and build an experimental system for testing. We adopt an FFT-based method for Monte Carlo generation of a random noise time-series signal with a prescribed power spectral density. This experimental system with noise inputs can be simulated in a way that is more consistent with the actual operation of the satellite in space. The experimental results show that laser pointing jitter noise mainly comes from the jitter of the satellite platform. In the 1 mHz to 1 Hz band, laser pointing jitter noise is an order of magnitude higher in the space environment than in the laboratory environment. However, the pointing system can still suppress the laser pointing jitter noise to the noise floor.

The stable and high-precision acquisition of attitude data is crucial for sustaining the long-term robustness of laser links to detect gravitational waves in space. We introduce an effective method that utilizes an adaptive weight optimization approach for the fusion of attitude data obtained from charge-coupled device (CCD) spot-positioning-based attitude measurements, differential power sensing (DPS), and differential wavefront sensing (DWS). This approach aims to obtain more robust and lower-noise-level attitude data. A system is designed based on the Michelson interferometer for link simulations; validation experiments are also conducted. The experimental results demonstrate that the fused data exhibit higher robustness. Even in the case of a single sensor failure, valid attitude data can still be obtained. Additionally, the fused data have lower noise levels, with root mean square errors of 9.5%, 37.4%, and 93.4% for the single CCD, DPS, and DWS noise errors, respectively.

We have conducted both theoretical and experimental investigations into the interaction between laser phase-locking and laser pointing techniques, which are crucial for space gravitational wave detection missions. Both techniques are essential for minimizing phase noise in the space interferometer, as the laser phase is the carrier of gravitational wave information. Introducing phase noise from either technique can significantly degrade the mission’s performance. To date, both techniques have been studied independently. Our research reveals the interaction that the laser phase-locking technique is sensitive to the laser pointing technique. The laser phase-locking technique suppresses one phase information from the quadrant photo diode (QPD) and disrupts the differential wave sensing (DWS) function for the laser pointing technique. Such circumstances indicate a need for re-evaluation of the current designs for both techniques.

Current methods for the high-precision real-time monitoring and parameter inversion of industrial methane point source leakage are insufficient. This research introduces a novel laser-based methane leakage monitoring approach for deployment on an unmanned aerial vehicle platform. An enhanced two-dimensional integral Gaussian diffusion model paired with a point sampling technique is employed to simultaneously determine the leakage rate and source location, integrating a genetic algorithm and an interior point penalty function algorithm for optimization. Simulations incorporating observational error sources are performed to quantitatively assess the accuracy of leakage parameter inversion under diverse errors, demonstrating the scheme’s viability. The accuracy of leakage parameter inversion achieved by the algorithm across various point sampling methods, gas plume characteristics, and wind speeds was examined, validating the assessment under multivariable influences in real observations. The proposed methodology was compared with two other leakage inversion optimization techniques, demonstrating its efficiency in addressing wind speed and directional effects. This study offers a practical method with significant implications for monitoring and quantifying industrial methane point source leakages.