In intelligent transportation system (ITS) applications, vehicles require robust and accurate positioning where global navigation satellite system signals are not available. However, despite extensive research, vehicle positioning with lane-level accuracy in a confined tunnel environment remains a challenging task. In this study, a positioning system based on a synchronized pseudolite system is proposed to enable low-latency and high-accuracy vehicle localization in tunnels. The pseudolites with cascading synchronization are deployed one-dimensionally. The effect of the cascading synchronization is analyzed; and a base station selection strategy is proposed to solve this problem. Fusion mechanization of the vehicle positioning system, digital elevation model, and laser ranging are adopted to provide three-dimensional positioning. Furthermore, deep integration is implemented using a low-cost inertial measurement unit to improve positioning accuracy and stability. The proposed localization solution is experimentally verified using a 4.6 km highway tunnel environment. The results of seven repeated experiments show that the positioning error is better than 11.55 cm.

Space-based systems providing remote sensing, communication, and navigation services are essential to the economy and national defense. Users’ demand for satellites has increased sharply in recent years, but resources such as storage, energy, and computation are limited. Therefore, an efficient resource scheduling strategy is urgently needed to satisfy users’ demands maximally and get high task execution benefits. A hierarchical scheduling method is proposed in this work, which combines improved ant colony optimization and an improved deep Q network. The proposed method considers the quality of current task execution and resource load balance. The entire resource scheduling process contains two steps, task allocation and resource scheduling in the timeline. The former mainly implements load balance by improved ant colony optimization, while the latter mainly accomplishes the high task completion rate by an improved deep Q network. Compared with several other heuristic algorithms, the proposed approach is proven to have advantages in terms of CPU runtime, task completion rate, and resource variance between satellites. In the simulation scenarios, the proposed method can achieve up to 97.3% task completion rate, with almost 50% of the CPU runtime required by HAW and HADRT. Furthermore, this method has successfully implemented load balance.

As foundational infrastructure for spatiotemporal information, the Global Navigation Satellite System (GNSS) delivers high-precision positioning, navigation, and timing (PNT) services worldwide. However, satellite atomic clock drift causes satellite clock bias, degrading PNT service quality. Compared to post-processed clock bias products and real-time estimation, satellite clock bias prediction offers a key advantage: it provides high-precision real-time clock bias even in scenarios with limited real-time data or poor communication. Through analysis and summarization of error sources in prediction models, this paper proposed generalized modeling frameworks for both classical and AI-based approaches. We reviewed current research on classical mathematical models—including polynomial, grey, Kalman filter, and time series models—and AI-based models such as machine learning (ML), multilayer perceptron (MLP), recurrent neural networks (RNN), and Transformer architectures. Technical characteristics, applicability, and limitations of each model were discussed. While AI-based models demonstrate superior flexibility and adaptability in complex scenarios compared to classical approaches, they require extensive datasets and computational resources. In conclusion, we summarized the advantages, disadvantages, and future research directions, offering insights for developing next-generation real-time high-precision GNSS PNT services.

High repetition rate dual-microcomb interferometry offers high-precision and fast acquisition, showing broad application prospects in rapid and precise ranging scenarios. In this study, a continuous-wave laser was coupled into two chip-based integrated silicon nitride microresonators to drive two dissipative Kerr soliton optical frequency combs. Their repetition rates, 24.96202 GHz and 24.96121 GHz, were locked to external reference RF sources. Using this dual-microcomb source, we conducted linear displacement experiments within a range of 150 millimeters, with residual errors less than 10 micrometers compared to a reference laser interferometer. In static measurements, the Allan deviation reached 1.6 nanometers at an averaging time of 40 milliseconds. Additionally, with an update rate exceeding 800 kHz, dynamic measurement capabilities were demonstrated by measuring the rapid rotating chopper. To the best of our knowledge, this study is the first to utilize a repetition-frequency-locked coherent dual-microcomb source for ranging experiments, significantly enhancing distance measurement accuracy to the nanometer level compared to free-running systems. The proposed system is of importance for future high-precision LiDAR and remote sensing applications based on dual microcombs, and holds potential in areas such as multi-satellite formation flying and microelectronic device manufacturing.

Theperformanceoftheglobalnavigationsatellitesystem(GNSS)canbeenhancedsignificantly by introducing the inter-satellite links (ISL) of a navigation constellation. In particular, the improvement of the position, velocity, and time accuracy, and the realization of autonomous functions require the ISL distance measurement data as the original input. For building a high-performance ISL, the ranging consistency between navigation satellites becomes a crucial problem to be addressed. Considering the frequency aging drift and the relativistic effect of the navigation satellite, the frequency and phase adjustment (FPA) instructions for the 10.23 MHz must be injected from the ground station to ensure the time synchronization of the navigation constellation. Moreover, the uncertainty of the initial phase each time the onboard clock equipment boots also results in a pseudo-range offset. In this Ref., we focus on the influence of the frequency and phase characteristics of the onboard clock equipment on the ranging consistency of the ISL and propose a phase compensation sensor design method for the phase offset. The simulation and experimental results show that the proposed method not only realized a phase compensation for the pseudo-range jitter, but, when the 1 PPS (1 pulse per second) falls in the 10.23 MHz skip area, also overcomes the problem of compensating the ambiguous phase by directly tracking the 10.23 MHz to ensure consistency in the ranging.

Accurate context awareness of land vehicles can assist integrated navigation systems. Motion behavior recognition is one context awareness of vehicles, based on which constraint information helps reduce the impact of short-term blockage of navigation signals on radio-frequency-based positioning systems. To improve the reliability of behavior recognition, we proposed a machine learning-based vehicle motion behavior recognition and constraint method (MLMRC). The machine learning-based recognition process is directly driven by raw data from low-cost MEMS-IMU, while the traditional threshold-based method relies on previous experience. Four categories of constraint information—sensor error calibration, velocity constraint, angle constraint, and position constraint—were constructed from the recognition results. Both the simulated vehicle experiment and real vehicle experiment demonstrate the performance of the MLMRC method. When there is a short-term blockage, the MLMRC method can reduce the positioning error from 17.2% to 38.3% compared with the traditional method, which effectively improves positioning accuracy and provides support for autonomous vehicles in complex urban environments.