A celestial navigation system (CNS) can be integrated in vehicles to suppress the diverging error of the inertial navigation system (INS). However, the CNS is subjected to aero-thermo-optic effects in hypersonic environments that introduce large-scale centroiding errors in star sensor images and greatly reduce the star sample size, limiting the usefulness of the CNS for hypersonic vehicles. In this study, these difficulties were addressed by the development of a statistical model for measurement noise based on the angular distance so that the standard deviation of the measurement noise can be correlated with the star sample size. To compensate for the centroiding error, a variable-dimension pseudo-transient adaptive filter was designed where the initial R matrix is set according to the star sample size. A semi-physical experiment was conducted to evaluate the performance of the proposed method, and the results indicated that stable centroiding error compensation was achieved for extremely small star sample sizes. In particular, the proposed method compensated for up to 66.24% of random positioning error, which is a performance improvement of about 10% compared with conventional compensation methods.

A segmented angular rate joint estimator for fusing the measurements in an inertial sensor array is presented with higher precision and lower calculation burden, which offers better performance to UAV(Unmanned Aerial Vehicle) navigation. In recent years, micro aerial vehicles are increasingly indispensable in various industries. Attitude control is crucial for normal flight, therefore the precision of angular rate measurement is the determinant for attitude calculation based on IMU. The rapid development of MEMS industry, in addition to heterogeneous sensors, has given birth to multiple methods for improving the precision and reliability of angular rate measurement using homogeneous sensors called inertial sensor array. Computing angular motion based on inertial sensor array including gyroscope and accelerometer triads is a joint optimal estimation process. Due to the scale effect of accelerometer array, it is also sensitive to the angular motion. Both of the 2 types of sensor can form a complementary filter at sensor level. In this paper, we have proposed EKF and LM framework for angular velocity estimate of inertial array system, and fused them in the form of subsection measurement to give full play to the characteristics of the two methods and two types of sensor. Monte-Carlo simulation shows that the precision of angular velocity estimate is improved by up to 4.57%, and the calculation amount of the algorithm is greatly reduced.

In this paper, a high-accuracy real-time vector map matching method based on polynomial fitting is proposed for autonomous driving. The real-time kinematic (RTK) positioning accuracy decreases due to the influence of multipath effects. Simultaneously, prolonged usage of the inertial navigation system results in a degradation of positioning accuracy, failing to meet the requirements of a Level 4 (L4) autonomous driving system. Consequently, the most effective method for carrier position is to integrate vector map matching created from high-precision positioning points, despite the fact that this method faces two distinct difficulties. First, a sparse description of vector maps in straight segment points causes reduced matching accuracy. Second, curved segment points are computationally intensive, and these data make it difficult to directly match vector maps. This paper uses a polynomial fitting map compression method that provides a new description of vector maps without increasing internal storage and computation to address the vector map matching problem. By using this method, the storage space of the map is compressed, and the positioning ability at the curve segment is enhanced. The results show that the positioning accuracy of missing fixed solutions is less than 20 cm, meeting the requirements of L4 autonomous driving vehicles without expensive lidar equipment.

Underground pipeline networks, as critical urban lifelines, require advanced inspection technologies to address structural degradation and corrosion. External information sources are rare in complex pipeline scenarios and constructing them is both costly and difficult. Reference points leads to inadequate centerline positioning precision and makes it challenging to meet defect point detection criteria. This study creates an adaptive error compensation model to successfully reduce the effects of alignment errors and heading drift errors in inertial navigation systems by building a dual cascade heading correction architecture that incorporates multisource data from miniature inertial measurement units (IMUs), odometers, and sparse GNSS reference points (SGRPs). Our tests show that the proposed technique preserves meter-level relative errors. Compared to the first-stage correction process, it significantly improves the ability of positioning error suppression in areas with intermittent GNSS signals by 70%. By using heading correction, the technique maintains convergence of the positioning error. Its position accuracy is 0.2% of the distance traversed in a 160-min pipeline experiment. This technology will promote the use of inexpensive discrete point correction technology in intelligent pipeline networks and set the groundwork for developing a scalable technology paradigm for the Internet of Things (IoT) in urban subterranean positioning.

With the development of vehicle sensors, unmanned driving has become a research hotspot. Positioning is also considered to be one of the most challenging directions in this field. Aiming at the poor positioning accuracy of vehicles under GNSS denied environments, a lane-level positioning method based on inertial system and vector map information fusion is proposed. A dead reckoning model based on optical fiber IMU and odometer is established, and its positioning error is regarded as a priori information. Furthermore, a map matching model based on HMM is built up. Three validation experiments are carried out and experimental results show that the positioning error can be reduced to less than 30 cm when driving for about 7 min, which proves the effectiveness of the proposed method. Our work may provide a reference for the further improvement of positioning for unmanned driving under GNSS denied environments.

The electromagnetic (EM) performance analysis is one of the core steps in the design of the radome. An efficient hybrid algorithm integrating ray tracing technique and physical optics method is proposed for the analysis of radome EM properties. First, the process flow of the proposed algorithm is introduced in detail. Then, the simulation experiment is performed and differences in radiation patterns obtained by different methods are also discussed. The experimental result verifies the efficiency and accuracy of the proposed algorithm.