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.

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.

The pseudolite system, an alternative to global navigation satellite systems, can provide centimeter-level positioning accuracy using carrier phase measurements. The carrier phase-based positioning accuracy is determined by the tracking performance of the phase-locked loop (PLL), since cycle slip may cause carrier phase measurement bias. To overcome the near-far problem, the pulsing scheme is often adopted in pseudolite positioning systems. For the pulsed pseudolite signal, the carrier tracking loop is more prone to cycle slip compared to the conventional continuous signal. For improving the performance of the PLL tracking the pulsed pseudolite signal at low carrier-to-noise ratios, a PLL with multi-pulse coherent integration (MPCI-PLL) is proposed. The thermal noise performance of the digital PLL (DPLL) is determined by its equivalent noise bandwidth. When the product of the design noise bandwidth and the loop update interval increases, the true equivalent noise bandwidth of DPLL tends to be larger than the design value. Then, the closed-form expression of the single-sided equivalent noise bandwidth of first-, second-, and third-order DPLL is derived by the discrete frequency domain approach. The proposed MPCI-PLL is tested in both simulations and a real experiment. Compared to the conventional method of tracking the pulsed pseudolite signal, the proposed carrier tracking loop significantly decreases the probability of a cycle slip at low signal-to-noise ratios. Simulation and experimental results show that the improvement is about 3–4 dB.