Intentional spoofing interference can cause damage to the navigation terminal and threaten the security of a global navigation satellite system (GNSS). For spoofing interference, an anti-spoofing algorithm based on pseudorange differences for a single receiver is proposed, which can be used to detect simplistic and intermediate spoofing attacks, as well as meaconing attacks. Double-difference models using the pseudorange of two adjacent epochs are established followed by the application of Taylor expansion to the position relationship between the satellite and the receiver (or the spoofer). The authenticity of the signal can be verified by comparing the results of the proposed spoofing detection algorithm with the traditional least squares method. The results will differ when spoofing is present. The parameter setting of the proposed algorithm is introduced. The algorithm has the advantage of both simplicity and efficiency and needs only a single receiver and pseudorange data. A NovAtel receiver is adopted for the actual experiments. The Texas spoofing test battery (TEXBAT), as well as two other simulation experiments are used to verify the performance of the algorithm. The simulation results validate the feasibility and effectiveness of the algorithm.

This paper focuses on the problem of high-update-rate and high accuracy inertial measurement unit signal generation. In order to be in accordance with the vehicle’s kinematic and dynamic characteristics as well as the characteristics of pseudorange of post-processed global navigation satellite system and their rate measurements, a novel dual quaternion interpolation and analytic integration algorithm based on actual flight data is proposed. The proposed method can simplify the piecewise analytical expressions of angular rates, angular increments and specific force integral increments. Norm corrections are adopted as constraint conditions to guarantee the accuracy of the signals. Numerical simulations are conducted to validate the method’s performance.

Accurate ego-motion estimation is critical for an autonomous robot. For this task, image-aided inertial navigation is a good solution. In this paper, the applicability and effectiveness of two algorithms for camera/IMU integration are compared under different ego-motion estimation context: the Multi-State Constraint Kalman Filter (MSCKF) and the Multi-View Geometry Constraint Kalman Filter (MVGCKF) based on Tri-focal Tensor Geometry. Both algorithms estimate the motion of a robot with an IMU (Inertial Measurement Unit), while observations of salient features from a monocular camera are used to correct the inertial navigation error in a Kalman filter. In contrast to traditional vision-aided inertial navigation algorithms, such as EKF-SLAM or the sliding window filter, the measurement models of the both two approaches do not include 3D feature information in filter states, in this way the computing complexity are reduced. We present experimental characterizations of the MSCKF and MVGCKF on KITTI dataset. Theoretical analysis and experimental results show that MSCKF is better for the low speed scenarios such as robot and pedestrian navigation, while MVGCKF is a better choice for the high dynamic and high speed environment such as unmanned land vehicles which need high update rate for navigational parameters. MVGCKF diverges rapidly at stationary and low speed state although its computation burden is less than that of MSCKF.