A low cost, high precision hollow structure MEMS IMU has been developed to measure the roll angular rate of a high-spinning projectile. The hollow MEMS IMU is realized by designing the scheme of non-centroid configuration of multiple accelerometers. Two dual-axis accelerometers are respectively mounted on the pitch axis and the yaw axis away from the center of mass of the high-spinning projectile. Three single-axis gyros are mounted orthogonal to each other to measure the angular rates, respectively. The roll gyro is not only used to judge the spinning direction, but also to measure and compensate for the low rotation speed of the high-spinning projectile. In order to improve the measurement accuracy of the sensor, the sensor output error is modeled and calibrated by the least square method. By analyzing the influence of noise statistical characteristics on angular rate solution accuracy, an adaptive unscented Kalman filter (AUKF) algorithm is proposed, which has a higher estimation accuracy than UKF algorithm. The feasibility of the method is verified by numerical simulation. By using the MEMS IMU device to build a semi-physical simulation platform, the solution accuracy of the angular rate is analyzed by simulating different rotation speeds of the projectile. Finally, the flight test is carried out on the rocket projectile with the hollow MEMS IMU. The test results show that the hollow MEMS IMU is reasonable and feasible, and it can calculate the roll angular rate in real time. Therefore, the hollow MEMS IMU designed in this paper has certain engineering application value for high-spinning projectiles.

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss.

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.

Aiming at the problems of the poor trajectory-tracking performance and low control accuracy of unmanned vehicles under complex working conditions, we first estimate the lateral force of tires using the square root cubature Kalman filter (SRCKF) in order to correct the lateral stiffness of the tires online, which reduces the model bias caused by constant lateral stiffness, and then adopt a Gaussian function-based adaptive time-domain model predictive control method to improve the trajectory-tracking control accuracy of unmanned vehicles under complex working conditions. Finally, the proposed control algorithm is validated via Carsim and MATLAB/Simulink joint simulation. The results show that compared with the classical model predictive control (MPC) algorithm, the proposed control algorithm reduces the average lateral tracking error by 73.07% and the peak beta and the peak yaw rate by 50.89% and 47.51%, respectively, so that the unmanned vehicle is able to maintain good tracking performance and control accuracy.

Aiming at the problem of attitude angular motion coupling of high-spinning rigid body, the model of the compound regular precession and the random angular motion is constructed by studying the angular motion characteristics of the high-spinning rigid body. This paper presents a new decoupling method with least squares angular rate zero-crossing detection. This method can estimate the coupling parameters in the model by estimating the angular motion period, and then extracts the real angular velocity information. In order to improve the attitude solution accuracy, a new four-subsample quaternion attitude algorithm is proposed by combining the angular velocity polynomial fitting and the attitude quaternion high-order Taylor series expansion. The self-developed high dynamic gyro and Missile-borne computer and simulation software are used to verify attitude angle resolution accuracy between the traditional method and the new method proposed in this paper. The results show that the method is superior to the traditional algorithm, and increase by an order of magnitude. Finally, the flight test with high dynamic gyro and Missile-borne computer is carried out to verify the real-time decoupling and attitude angle real-time solving ability of the proposed algorithm in the flight test.