As one of the state-of-the-art technologies currently being studied in the field of target positioning, the entangled light quantum-based passive target positioning method uses quantum entangled states to detect and localize the target, thus providing highly-accurate and anti-interference positioning services. The conventional methods seldom consider the problem that maybe a pseudo-target is detected when there is actually no target due to the interference of particles, scatterers, and noise photons during light quantum transmission. To address this problem, this paper proposes a new passive target positioning method on uncertain detection path based on entangled light quantum. Specifically, first of all, by converting the continuous light energy into the discrete photon number, the photon return rate is used to calculate the number of arrival photons based on the counting characteristics of the single photon detector. Second, the detection probabilities of signal, idler, and noise photons are calculated to obtain the coincidence probability at the receiver. Third, according to the coincidence count value of the entangled light time pulse sequence, the Constant False Alarm Rate (CFAR) detection model is established, and the corresponding detection threshold is adaptively adjusted with the change of the background noise to achieve highly-sensitive target detection. Finally, the relative error for each light source is considered to select the optimal light sources with the smallest relative distance errors for target positioning. In addition, the results show that the detection probability of the proposed method is over 95% with the false alarm probability 0.05, the positioning error is centimeter-level, and the confidence probability within 2.60 cm can reach 92%.

This paper proposes a terahertz absorber based on a U-shaped metal patch that exhibits both narrowband and wideband absorption characteristics. The absorber comprises a metal–medium–metal structure, with a U-shaped metal patch as the top layer, a lossy medium as the middle layer, and a fully covered metal base plate at the bottom. There are two narrowband absorption peaks at 1.368 THz and 2.648 THz and a broadband absorption band from approximately 4 to 5 THz. The formation of resonance frequencies was elucidated by analyzing the current and electric field distributions at the resonance frequency. The resonant mechanism of the U-shaped patterned structure was clarified using equivalent circuit models to evaluate the resonant frequencies of different absorption peaks. Subsequently, the absorber’s characteristics were examined under varying structural parameters. Dual-function terahertz absorbers are applicable in terahertz communications, imaging, and various other fields.

As unmanned robotic operations become prevalent in various fields, the presence of moving objects in complex scenes poses challenges to key technologies, such as environment mapping, obstacle avoidance, and trajectory prediction. In this article, we propose FP-MOS, a LiDAR-based dynamic object segmentation framework that integrates prediction information to continuously capture dynamic object information under constrained data conditions. First, a prediction module (PM) is employed to obtain predicted range images, enhancing the continuity of temporal information in the point cloud within the segmentation network. This improved continuity helps mitigate misjudgment issues caused by dynamic object turns and retrieves previously missed moving objects. Additionally, a motion attention weight guidance module is introduced, which accurately captures dynamic point cloud features through the transmission of point cloud weights between adjacent frames. Finally, we apply an improved adaptive local outlier factor (ALOF) method to filter out outliers in the segmentation results. Experimental results on the SemanticKITTI-MOS and Apollo datasets show that our method achieves leading Intersection-over-Union (IoU) scores of 79.3% and 79.0%, demonstrating the effectiveness of the FP-MOS method in network design and data optimization.

The sparsity of the localization problem makes the Compression Sensing (CS) theory suitable for indoor localization in Wireless Local Area Networks (WLAN). However, in practice, we find that the location errors and computing complexity increase significantly as the dimensionality of the sparse vector and measurement matrix are high in a large environment, so most CS-based localization techniques are accompanied by coarse localization and AP selection stages. Therefore, in this paper, we first deduced the relationship between the number of Access Points (APs) and the dimensionality of the sparse vector theoretically to give the guideline that the number of sub-databases and APs should be obtained. Then an Adaptive Intuitionistic Fuzzy C-ordered Mean (AIFCOM) clustering is designed for the data with outliers in the environment with multipath effects. Finally, in the fine localization stage, we propose a Semi-tensor Product Compression Sensing (STP-CS) model to construct the measurement matrix, compared with the traditional CS model, our model not only remains more number of APs, but also decreases the dimensionality of measurement matrix, which can reduce the storage space and improve localization accuracy simultaneously.

Hollow corner-cube retroreflectors (HCCRs) are an essential reflection component of next-generation lunar laser-ranging technology. The verticality among the three reflectors, known as the right-angle error, is a critical parameter that affects the emission performance, and thus, should be correctly measured and calibrated. However, conventional methods measure the three right-angle errors separately, which can induce error superposition during the measurement process. A one-time measurement method was developed for the three right-angle errors of the HCCR using a single autocollimator (AC). The method establishes a mathematical relationship between the right-angle error of the HCCR and the angle offset of the reflected beam, and it considers the observation coordinates of the AC simultaneously to perform the coordinate transformation of the relationship parameters. The corresponding measurement equation was derived to extract the three-plane right-angle error of the HCCR using the measured readings of a single AC. In addition, a HCCR was designed to freely adjust the angle of the two reflective surfaces and used to simulate the different states of the three right-angle errors in practice. The measurement results show that the root-mean-square error of the proposed method in all right-angle error states is smaller than 16^′′.