Due to the complexity of urban environments, simple global navigation satellite system/inertial measurement unit (GNSS/IMU) cannot meet the positioning demand in urban environments. Light detection and ranging(LiDAR) can perceive the surrounding environment in real-time, and its cost has been continuously decreasing in recent years. Due to the good complementary characteristics, GNSS/IMU/LiDAR has been widely studied. An improved point cloud registration LiDAR assisted GNSS/IMU integrated navigation method is proposed. GNSS quality control is done by the algorithm by adding error terms in LiDAR registration, creating local point clouds, developing double difference feature quantities to evaluate satellite signals thoroughly, and achieving GNSS signal weighting for various signal qualities. Experimental results show that in urban environments, the proposed algorithm achieves a 49.33% improvement in horizontal position accuracy and a 48.31% improvement in 3D position accuracy compared to traditional GNSS/IMU algorithms. Compared to traditional GNSS/IMU/LiDAR algorithms, the horizontal 3D position accuracy has been improved by 40.33% and 37.60%, respectively.

In complex urban environments, tall buildings cause Global Navigation Satellite System (GNSS) signals to suffer from Non-Line-of-Sight (NLOS) propagation and Multipath Interference (MI), which degrade the positioning accuracy of intelligent transportation systems. The existing two-dimensional grid-based multipath modeling method has shortcomings including insufficient precision in the height direction and an overly simplistic adjustment strategy for measurement noise covariance. This article proposes a GNSS/inertial measurement unit (IMU) robust adaptive filter algorithm based on 3D grid error modeling. By dividing the height space on the basis of the existing 2D grid, fine modeling can be further achieved. In the stage of multipath error prediction, the grid-center-matching method is used to alleviate the model prediction error caused by incorrect matching. Then, we propose a filtering model selection strategy based on the multipath error predictions. Moreover, according to the robust theory, we propose a robust threshold dynamic adjustment strategy to update the measurement noise covariance adaptively. The positioning performance of GNSS/IMU integrated navigation in complex urban environments can be significantly improved by the proposed algorithm. The 3D positioning accuracy of the suggested algorithm has improved by 27.57% when compared to the 2D grid assisted GNSS/IMU robust adaptive algorithm and by 48.44% and 31.51%, respectively, when compared to the traditional GNSS/IMU tight combination algorithm and the traditional GNSS/IMU robust adaptive algorithm, according to the results of urban environment vehicle experiments.

In the complex urban environment, Global navigation satellite system (GNSS) signals are prone to non-line-of-sight reception (NLOS) and multipath interference (MI) due to the occlusion and reflection of obstacles such as tall buildings. The positioning accuracy and reliability are seriously reduced, which cannot meet the user’s high precision and reliability positioning, navigation and timing (PNT) service requirements. Signal classification and multipath modeling methods based on machine learning and data-driven are of great significance for alleviating GNSS multipath effect and improving positioning accuracy in urban areas. But the accuracy, efficiency and adaptivity of such models still need to be improved. In this paper, a grid error modeling aided GNSS/Inertial measurement unit (IMU) integrated navigation comprehensive quality control algorithm is proposed. Besides the grid pseudorange error modeling, it proposes a refined comprehensive quality control strategy based on grid fitting accuracy and satellite fault detection, thus optimizes the performance of GNSS/IMU integrated navigation in complex urban environments. The field test in urban environment shows that compared with the traditional GNSS/IMU integrated navigation algorithm, the horizontal and 3D positioning accuracy of the proposed algorithm are improved by 50.23% and 66.77%, respectively, and compared with the grid pseudorange error modeling algorithm, they are improved by 11.56% and 40.53%, respectively.

In global navigation satellite system (GNSS) and inertial measurement units (IMU) integrated navigation systems, attitude estimation, especially the accurate heading estimation, is very important for the real-time monitoring of vehicle state. However, due to the divergence of IMU on the altitude channel, its errors will gradually accumulate if the IMU cannot be accurately constrained, so the estimation accuracy of the heading is insufficient in the vehicle application where the heading changes frequently. In order to solve the problem of poor attitude estimation accuracy in GNSS and IMU loosely coupled navigation, a heading enhancement algorithm of GNSS/IMU integrated navigation based on dual-antenna time-differenced carrier phase (TDCP) is proposed. The vehicle heading is solved by dual-antenna TDCP to increase the input dimension of observations in integrated navigation filtering, and the Hatch filter and robust adaptive filter are used to improve the pseudorange accuracy of the observation domain and the GNSS/IMU integrated navigation positioning and attitude estimation performance, respectively. The algorithm evaluation results based on the measured data show that compared with the traditional GNSS/IMU integrated navigation method, the proposed algorithm improves the positioning and velocity measurement accuracy by 22.12% and 41.27%, respectively, and the heading accuracy by 46.29%.

Safety-critical intelligent transportation systems (ITS) applications are surging in recent years. These kinds of applications not only have the accuracy but also the integrity of global navigation satellite system (GNSS) positioning services. In the field of aviation with an open environment, advanced receiver autonomous integrity monitoring (ARAIM) has been widely concerned as a low-cost, highly autonomous integrity monitoring method. However, there are still gaps in the application of urban environments. Moreover, the probability of integrity risk and continuity risk of traditional ARAIM algorithm which is applied for aviation applications in the open environment has been equally allocated, resulting in the relatively conservative protection level. In order to solve the above problems, this paper proposes a protection-level optimization method based on Teaching-learning-based optimization (TLBO), which can realize the reasonable allocation of integrity risk and continuity risk under the integrity requirements of urban road safety, so as to improve the availability of multi-constellation ARAIM. The on-board measured data shows that under the global position system（GPS） + Galileo satellite navigation system (GAL) dual constellation scenario, the average optimization rates of horizontal protection level (HPL) and vertical protection level (VPL) are 50.58% and 44.14%, and the availability of ARAIM for the 10-meter alert limit (AL) is increased by 51.29%. In the GPS+GAL+BDS multi-constellation scenario, the average optimization rates of HPL and VPL are 59.59% and 56.33%, and the availability of ARAIM for the 10-meter AL is improved by 99.29%.