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As Global Navigation Satellite System (GNSS) spoofing techniques are highly stealthy and pose a tremendous risk to targets using GNSS technology, studies on GNSS spoofing techniques have been in the spotlight. If the accurate position and velocity of the target receiver can be obtained, the target receiver can be covertly spoofed during the signal tracking stage using synchronous lift-off spoofing. However, it is often difficult to accurately obtain the position and velocity of a target in real GNSS spoofing scenarios. To address this problem, To study the effects of spoofing signals' power (relative to the real signal), code pulling rate, carrier Doppler shift, initial code phase difference, and carrier phase difference on the efficacy of spoofing, the intrusion of receiver's signal tracking loop by spoofing signals is mathematically modeled. Based on the model, an asynchronous lift-off spoofing for GNSS receivers in the signal tracking stage is proposed. Theoretical analysis and experimental results show that the new method resulted in stable Doppler frequency variations, short fluctuations in carrier-to-noise ratio (C/N) and signal lock time, and gentle changes to the receiver's 3D Earth-Centered Earth Fixed (ECEF) coordinates, when the target's position and velocity were approximately known during the intrusion period. The proposed spoofing method is highly feasible and could expand the scope of applicability of lift-off spoofing.

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.

For classical TCAR(three carrier ambiguity resolution) algorithm is affected by ionospheric delay and measurement noise, it is difficult to reliably fix ambiguity at medium and long baselines. An improved TCAR algorithm which takes the influence of ionospheric delay into account and has good adaptive robustness is proposed. On the basis of the non-geometric TCAR model, ionospheric delay is obtained by linearly combining extra-wide-lane with fixed ambiguity, and then wide-lane ambiguity is solved. Solving narrow-lane ambiguity by adaptive robust filtering by constructing optimal combination observation, which can effectively improve the fixed success rate of narrow-lane ambiguity and reduce the adverse effects of gross error. Experimental results show that the improved TCAR algorithm can guarantee a high fixed correct rate of wide-lane ambiguity, effectively improve fixed success rate of narrow-lane ambiguity, and has a good ability to resist gross error.

Satellite navigation spoofing has become a central issue of jamming technology research because of its serious threat and ability to conceal itself. Increasingly, targets are equipped with more robust GNSS/IMU systems and normalized innovation squared (NIS) is used to detect interference. Therefore, it is harder to implement covert trajectory spoofing on a GNSS/IMU system than a GNSS-only target. In practice, spoofing is needed to control unknown targets. Therefore, covert trajectory spoofing for GNSS/IMU targets is an important issue. Hence, using the information fusion of a GNSS/IMU system, the influence of spoofing on loosely coupled GNSS/IMU positioning is derived. To avoid ill-posed equations when introducing a measurement deviation, a Kalman gain matrix local regularization method is proposed to accurately determine the measurement deviation. To avoid triggering the NIS detection alarm, the range that enables the introduced measurement deviation to remain concealed is calculated. Then, a two-step trajectory guidance algorithm is proposed to quickly guide the target onto the spoofing trajectory. The simulation results show that the proposed trajectory spoofing algorithm can guide a loosely coupled GNSS/IMU target along a spoofing trajectory without triggering the NIS detection alarm. The proposed method can remain concealed and has good theoretical and practical application value.

This paper proposes an error analysis method based on epipolar geometry which is analyzed for improving the accuracy of attitude estimation. The influence of equivalent focal length and principal point coordinates on modeling error was analyzed by simulation and experiment based on a rotary table. The result shows that the influence of the equivalent focal length will increase as the rotation angle increases, the different motion states will lead to different degrees of impact of the principal point coordinates. Besides, we derived that pose measuring accuracy is unrelated to the magnitude of rotation angle and there is no accumulative error under simulation conditions. This proposed method can enhance the measuring accuracy and provide some meaningful advice for engineering applications.

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.

Robust control of a class of uncertain Takagi-Sugeno (T-S) fuzzy system is investigated in the paper by using fuzzy region concept. The general uncertain T-S fuzzy model with Standard Fuzzy Partition (SFP) inputs is converted into fuzzy region ones. A Fuzzy Region Controller (FRC) is designed based on the fuzzy region concept, and sub-models in a fuzzy region share the same FRC. The relaxed stability conditions for closed-loop fuzzy region systems are derived by using Piecewise Smooth Quadratic (PSQ) Lyapunov function in terms of Linear Matrix Inequalities (LMIs). The derived stability condition, which only requires finding a local common positive definite symmetric matrix P in each operating region, can reduce the conservatism and difficulty in existing stability conditions. Finally, the feasibility and validity of this approach are demonstrated by a numerical example.