A typical calibration scheme for a gimbaled inertial measurement unit (IMU) involves an estimation of error parameters of an IMU mounted on an inertial platform and the platform's misalignment angles. However, traditional calibration methods for the gimbaled IMU have some serious defects. The excitation for a gyro's scale factors and misalignment angles is only the Earth rate in multi-position calibration methods and dynamic errors (unneeded motion of gyro floaters) involved in a continuous calibration process. This paper presents a new continuous self-calibration scheme for the gimbaled IMU. By processing the multi-position and continuous rotation steps alternately, the dynamic errors are suppressed and the excitation is augmented. This is more effective than traditional methods. Additionally, the platform rotation trajectory is designed to provide adequate observability for all parameters through a new methodology. The Lie derivative is used to compute the observability, and the genetic algorithm is utilized to obtain the inertial platform's optimal rotation trajectory based on the measurement of observability for all parameters. Simulation results show that the error coefficients can be effectively calibrated within an hour by the proposed scheme, and it is of high significance for fast launching of missiles and rockets.

In recent years, location prediction has become an important task and has gained significant attention. Existing location prediction methods rely on centralized storage of user mobility data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this work, we propose a privacy-preserving method for mobility prediction model training based on federated learning, which can leverage the useful information in the behaviors of massive users to train accurate mobility prediction models and meanwhile remove the need to centralized storage of them. Firstly, we propose a novel network named STSAN (Spatial-Temporal Self-Attention Network) on each user device, which can integrate spatiotemporal information with the self-attention for location prediction and a new personalized federated learning model named AMF (Adaptive Model Fusion Federated Learning), which is a mixture of local and global model. Finally, the results are superior to various baselines on four realworld check-ins datasets, verifying the effectiveness of the method. CCS CONCEPTS • Security and privacy → Human and societal aspects of security and privacy; • Information systems → Location based services; • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods.

High precision inertial navigation system is one of the main ways to improve the accuracy of the satellite orbit and to prolong life. In order to solve the suspension stabilization problem of the floated inertial navigation platform (or the floated platform), a robust finite time second-order sliding mode control scheme is proposed. This control method can guarantee the stability and rapidity of the system under the complex disturbances. Then, focusing on the design problem of the observer, the nonlinear extended state observer is designed to enhance the adaptability for random uncertainty and to improve the robust performance and the stabilization accuracy of the controller. Finally, the stability and convergence of the control system are proven by the homogeneous theory. The simulation results demonstrate that the proposed method can eliminate the input chattering of the sliding mode control efficiently and the high precision inertial stabilization of the floated inertial platform is realized with the accuracy higher than 0:05.

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