Satellite integrity monitoring is vital to satellite-based augmentation systems, and can provide the confidence of the differential corrections for each monitored satellite satisfying the stringent safety-of-life requirements. Satellite integrity information includes the user differential range error and the clock-ephemeris covariance which are used to deduce integrity probability. However, the existing direct statistic methods suffer from a low integrity bounding percentage. To address this problem, we develop an improved covariance-based method to determine satellite integrity information and evaluate its performance in the range domain and position domain. Compared with the direct statistic method, the integrity bounding percentage is improved by 24.91% and the availability by 5.63%. Compared with the covariance-based method, the convergence rate for the user differential range error is improved by 8.04%. The proposed method is useful for the satellite integrity monitoring of a satellite-based augmentation system.

To ensure the safety of Global Navigation Satellite System (GNSS) users, spoofing detection is generally performed using additional correlators, where a detection metric is built to analyze the distortion of the cross-correlation function. Increasing the probability of detection is usually achieved by increasing the number of additional correlators, and yet such measure can induce a heavy computational burden. To address this limitation, we propose a sliding composite Delta (SCD) metric, where the threshold is obtained by maximum likelihood estimation (MLE). Using only eight correlators, the proposed metric exhibits sufficient superiority in probability of detection. The interval between the additional correlators can be regularly adjusted to build a composite detection metric, which can facilitate the detection of spoofing signals with varying delays. In addition, the adopted MLE-based method allows for the determination of an optimal detection threshold. Simulation and experiment results show that, compared with the existing metrics, the proposed metric achieves a higher probability of detection and a shorter time to alarm. Therefore, the proposed metric is more suitable for spoofing detection.

A novel magnetic coupling resonance wireless power transfer (MCR‐WPT) system, which has constant‐current (CC) and constant‐voltage (CV) output characteristics suiting for batteries charging, is proposed in this paper. The hybrid compensation topology is adopted; in detail, the LCL‐P circuit topology is used in the CC mode, while the LCL‐S topology is used in the CV mode. Since the switchable components are only located at the receiving side of WPT, the switching can be controlled by the receiving side based on the batteries’ state without data communication between transmitting side and receiving side. Furthermore, an adjustable frequency tuning method which is based on proportion integration (PI) closed‐loop controlled by the transmitting side is applied to maximize the current of the transmitting coil. This method also can solve the frequency splitting problem, improving the stability and power of the system while the distance of the two sides changes. The performance of the proposed WPT system is verified by simulation and experiments. The experiment results show that the load current variation is less than 3% in the CC mode and the load voltage changes less than 5% in the CV mode, as the load resister changes from 5 to 20 Ω.

The BeiDou Navigation Satellite System (BDS-3) has provided positioning, navigation and timing (PNT) services to global users across land, maritime, and aviation. However, how to classify these three types of users with complex movement patterns poses great challenges to the work of monitoring and evaluating the PNT system. To accurately classify multi-type global navigation satellite system (GNSS) users, this paper proposes a method that combines random sample consensus (RANSAC) and K-means clustering to track the movements of massive users and classify them based on their dynamic characteristics in different areas, which is noted as RANSAC-K-means. The simulated massive user data show that the recognition rate of the proposed algorithm exceeds 83.22%, and compared with the conventional method, the proposed RANSAC-K-means method improved the recognition rate by 11.16%. The RANSAC-K-means method can provide more accurate clustering results under the situations where multi-type users present dynamic characteristics with significant differences, showing significant stability and robustness. The proposed method is more suitable for monitoring and evaluating the service performance of satellite navigation systems.