GNSS/INS/LiDAR-based Multi-Sensor Fusion (MSF) systems facilitate the efficient integration of multiple navigation sensors to deliver stable and dependable positioning outcomes in Autonomous Vehicles (AVs). Generally, AVs are bound to operate in diverse scenarios. However, state-of-the-art spoofing attack algorithms can identify vulnerable periods of MSF systems and execute aggressive GNSS spoofing in certain specific scenarios with high success rates. The present study proposes a security defense algorithm for MSF systems based on an enhanced Kalman filter, incorporating the Measurement Variance Monitoring (MVM) and Minimum Constraint of Covariance Matrix (MCCM) model. MVM effectively constrains the impact of GNSS signals with large positioning errors, thereby mitigating the adverse effects caused by spoofing attacks. MCCM fully leverages LiDAR information between two consecutive GNSS signals to enhance the credibility of LiDAR measurements, thus improving the positioning correction capability. Furthermore, considering the diverse application environments of AVs, this paper primarily focuses on sensor uncertainty variation and validates different models in varying scenarios. Ultimately, real-world data demonstrate the effectiveness of the proposed anti-spoofing model, which can significantly mitigate positioning errors caused by GNSS spoofing attacks without affecting navigation accuracy.

In the leader–follower-based multiple underwater unmanned vehicles (UUVs) cooperative positioning system, the precision of the compass and Doppler velocity log equipped with the follower UUV is low, which will inevitably lead to yaw error and velocity error (YEaVE) in the output data. These errors will seriously affect the positioning accuracy of follower UUV. In addition, the observation information often has many outliers due to the influence of underwater multipath, which will cause the increase of the positioning error. Herein, we propose a novel adaptive sliding observation-based cooperative positioning algorithm under the factor graph framework. First, the influence mechanism of YEaVE on positioning accuracy of the follower UUV is analyzed. Thereafter, under the factor graph framework, a cooperative positioning algorithm based on maximum correntropy criterion is designed in which the estimation and compensation of YEaVE are innovatively added. At last, an adaptive sliding observation method is designed to deal with outliers in observation information. The proposed algorithm is verified by simulations and shipboard experiment, and the results show that it can accurately estimate the YEaVE. At the same time, compared with the ordinary factor graph algorithm, the positioning accuracy of the proposed algorithm has been improved by 88.31% and 81.63% in simulation and shipboard experiment.

With the advantages of anti-interference and no accumulated error, bionic polarization heading measurement has important military significance and research value for autonomous navigation. However, the heading robustness is seriously affected by the accuracy of skylight polarization model and the existing methods all use the Rayleigh scattering model without considering the atmospheric depolarization. Therefore, this paper proposed an innovative method of polarization heading measurement based on Berry model to considering the influence of depolarization neutral points. And this model is improved by controlling the neutral points with correction coefficients to realize high-robustness heading measurement. On this basis, the improved Berry model is further used to calibrate the sensor parameters outdoors, which separates the skylight polarization model error from the sensor error without expensive instruments and complicated processes, and improves the heading measurement accuracy. In the experiments with changing solar altitude, the average of heading error STD after field calibration is 20.86% lower than that of Berry model and 92.85% lower than that of Rayleigh model, which shows great advancement in actual measurements.

The Imaging Polarization Sensor (IPS) can provide high-accuracy, robust, and fully autonomous navigation information for underwater unmanned platforms. However, the polarization images in complex underwater scenarios are affected by dynamic-wave interference, severely impacting the accuracy of underwater polarization orientation. Therefore, a suppression method of dynamic-wave interference based on angular increment assistance is proposed. Firstly, the transmission model of polarized light under dynamic-wave interference is constructed, and the error mechanism of the Angle of Polarization (AOP) is analyzed. Then, combining the inertial angular increment, a total differential increment model of AOP under the condition of carrier motion is established and used as the theoretical prior for the relative change in adjacent frames. On this basis, an AOP deviation estimation method based on complementary filtering is proposed, which exploits the complementary characteristics between the total differential increment of AOP and the actual detected values, achieving dynamic-wave suppression with single-frame input and thereby improving the accuracy of polarization orientation. In actual underwater experiments, the proposed method not only effectively improves the quality of AOP images but also significantly enhances the orientation accuracy. Compared to the existing optimal methods, the underwater static orientation error and the underwater dynamic orientation error are reduced by 26.3% and 33.4%, respectively.

High-precision mapping and positioning of urban underground pipelines are the basic requirements of urban digital construction. Aiming at the above problems, a dead reckoning algorithm based on the starting point and ending point correction and forward and reverse solution information reuse is proposed. This paper firstly establishes a dead reckoning system model consisting of a microelectromechanical system (MEMS) inertial measurement unit (IMU) and an odometer and analyzes the propagation mechanism of dead reckoning errors. The algorithm constructs the trajectory correction matrix by using the position information of the starting point and the ending point of the short-distance underground pipeline and then uses the trajectory correction matrix to correct the trajectory position information obtained by forward and reverse dead reckoning. Finally, the corrected forward and reverse trajectory position information is fused and averaged to achieve high-precision mapping and positioning of underground pipelines. The simulation results of the 100 m pipeline show that the maximum positioning error of the proposed algorithm for straight pipelines is within 5 cm, and the maximum positioning error for 90° curved pipelines is within 20 cm. The algorithm effectively solves the problem of a rapid accumulation of errors over time in the process of dead reckoning, which greatly improves the positioning accuracy.