In recent years, various new portable global navigation satellite system (GNSS) spoofers have emerged that have become an important threat to the integrated inertial navigation system (INS)/GNSS. The spoofing strategies have become increasingly advanced and the spoofer gradually adjusts the counterfeit signals to covertly take over the target receiver. The existing anti-spoofing algorithms based on INS/GNSS integration show the shortcomings of the reduced detection probability, and the inability to mitigate spoofing. In this study, the multipath estimating delay lock loop (MEDLL), originally designed for multipath mitigation is used in conjunction with INS to suppress the spoofing attacks. First, a bank of correlators is employed to estimate the spoofing and authentic signals. Second, a new spoofing validation and mitigation structure is proposed based on the tightly coupled INS/GNSS integrated system. Finally, the INS-aided reacquisition and spoofing suppression method is derived. The simulated INS measurements were used together with Texas Spoofing Test Battery datasets to evaluate the performance of the proposed anti-spoofing algorithm. The results showed that the spoofing attacks were detected within 1 s and the positioning errors decreased from 600 m to 12.9 m. The proposed algorithm can suppress different types of spoofing attacks and ensure reliable positioning and timing results in various complex threat environments.

A navigation grade Strapdown Inertial Navigation System (SINS) combined with a Doppler Velocity Log (DVL) is widely used for autonomous navigation of underwater vehicles. Whether the DVL is able to provide continuous velocity measurements is of crucial importance to the integrated navigation precision. Considering that the DVL may fail during the missions, a novel neural network-based SINS/DVL integrated navigation approach is proposed. The nonlinear autoregressive exogenous (NARX) neural network, which is able to provide reliable predictions, is employed. While the DVL is available, the neural network is trained by the body frame velocity and its increment from the SINS and the DVL measurements. Once the DVL fails, the well trained network is able to forecast the velocity which can be used for the subsequent navigation. From the experimental results, it is clearly shown that the neural network is able to provide reliable velocity predictions for about 200 s–300 s during DVL malfunction and hence maintain the short-term accuracy of the integrated navigation.

Owing to the nonlinearity in visual-inertial state estimation, sufficiently accurate initial states, especially the spatial and temporal parameters between IMU (Inertial Measurement Unit) and camera, should be provided to avoid divergence. Moreover, these parameters are required to be calibrated online since they are likely to vary once the mechanical configuration slightly changes. Recently, direct approaches have gained popularity for their better performance than feature-based approaches in little-texture or low-illumination environments, taking advantage of tracking pixels directly. Based on these considerations, we perform a direct version of monocular VIO (Visual-inertial Odometry), and propose a novel approach to initialize the spatial-temporal parameters and estimate them with all other variables of interest (IMU pose, point inverse depth, etc.). We highlight that our approach is able to perform robust and accurate initialization and online calibration for the spatial and temporal parameters without utilizing any prior information, and also achieves high-precision estimates even when large temporal offset occurs. The performance of the proposed approach was verified through the public UAV (Unmanned Aerial Vehicle) dataset.

Under harsh propagation conditions, Global Navigation Satellite Systems (GNSS) receivers are susceptible to signal degradation, which can lead to loss of signal lock. Unlocked receivers, when subjected to spoofing attacks, are highly susceptible to false re-acquisition, and their tracking-stage anti-spoofing algorithms may become ineffective, thereby simplifying the execution of spoofing interference. Compared to the traditional scalar tracking loop (STL), the vector tracking loop (VTL) demonstrates superior performance in maintaining Positioning, Navigation, and Timing (PNT) service continuity in complex environments. Therefore, this study designs an enhanced VTL by developing an adaptive loop controller and constructs a composite interference scenario using the Oak Ridge Spoofing and Interference Test Battery (OAKBAT) database to compare the anti-spoofing performance of STL and VTL under various application challenges. In the os2 scenario, the STL’s root mean square (RMS) value of the receiver clock error increases sharply to 601.1 m owing to the re-acquisition, while the VTL is not affected by the signal interruption. In the os4 scenario, the RMS value of its position solution deviation was reduced by 66.35% and 80.83% compared with that of the STL before and after signal interruption, respectively. The study demonstrates that VTL, empowered by an Extended Kalman Filter (EKF) and interchannel cooperation, possesses significant anti-spoofing advantages in adverse environments. These findings highlight the significant advantages of VTL in improving the endogenous security of GNSS receivers and provide key insights for designing anti-spoofing algorithms for challenging environments.

Once the spoofer has controlled the navigation system of unmanned aerial vehicle (UAV), it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error. Aiming at this problem, the influence of the spoofer's state estimation error on spoofing effect and error convergence conditions is theoretically analyzed, and an improved adaptively robust estimation algorithm suitable for steady-state linear quadratic estimator is proposed. It enables the spoofer's estimator to reliably estimate UAV status in real time, improves the robustness of the estimator in responding to observation errors, and accelerates the convergence time of error control. Simulation experiments show that the mean value of normalized innovation squared (NIS) is reduced by 88.5%, and the convergence time of NIS value is reduced by 76.3%, the convergence time of true trajectory error of UAV is reduced by 42.3%, the convergence time of estimated trajectory error of UAV is reduced by 67.4%, the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%, and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8% when the improved algorithm is used. The improved algorithm can make UAV deviate from preset trajectory to spoofing trajectory more effectively and more subtly.

An experimental analysis method for the correlation peak distortion of the BeiDou‐3 satellite navigation system (BDS‐3) B1C signal is proposed to address the gaps in existing research—lack of experimental validation and insufficient insights into tracking loop distortion mechanisms under spoofing. First, the autocorrelation characteristics of the B1C signal under narrow band (NB) and wide band (WB) tracking modes were analysed based on its modulation characteristics. A feeder‐based BDS spoofing experimental platform was constructed to accurately simulate real‐world spoofing scenarios and collect high‐fidelity intermediate frequency (IF) data. Multi‐correlator tracking outputs were utilised to quantify the distortion effects of spoofing types and tracking modes on the B1C tracking loop. Experimental results revealed three key findings: (1) during spoofing takeover, the combined correlation peak oscillates at high frequency (spoofing as reference, residual true signal as amplitude); (2) under position spoofing, correlation peak pulling direction/rate vary by satellite; under time spoofing, the pulling is consistent across satellites; (3) WB tracking offers higher ranging accuracy than NB tracking but is more susceptible to spoofing‐induced pulling and exhibits a more complex correlation peak distortion. These results provide a theoretical and experimental basis for the design of BDS‐3 anti‐spoofing algorithms and equipment optimisation.