To improve the alignment accuracy and environmental applicability of the micro-electromechanical system (MEMS)-based strapdown inertial navigation system (SINS), this study proposes a global navigation satellite system (GNSS)-assisted multi-vector determination of the attitude optimal indirect coarse alignment. Using the inertial information output from the inertial measurement unit and the velocity information from the GNSS, a simplified velocity observation vector is constructed and the velocity lever arm stemming from the mounting position inconsistencies of the GNSS and SINS is fed back into the velocity of the carrier system. When constructing the observation vectors, the integration interval is shortened by reconstructing the two-vector integration formula for reducing the cumulative error of the inertial device. The attitude matrix problem is defined as the Wahba problem, which is solved using the singular value decomposition method. Based on the relationship between gyro zero bias and misalignment angle, the corresponding state and measurement equations are designed. Furthermore, owing to the measurement noise uncertainty, the adaptive traceless Kalman filtering algorithm is introduced to realize the effective adaptation processing of the measurement noise. More accurate attitude matrix estimates are obtained by continuously correcting the carrier system transformation matrix. The running car experiment results show that the proposed method exhibits higher alignment accuracy and environmental applicability than the current MEMS strapdown inertial navigation coarse alignment method and the traditional optimization-based alignment method.

At present, integrating Global Navigation Satellite System (GNSS), micro-electromechanical system (MEMS), and odometer (OD) is the most practical and low-cost vehicle multi fusion navigation system. However, time-varying noise due to satellite signal rejection can lead to serious degradation or even inaccurate positioning accuracy of the system. To overcome this problem, an adaptive Spherical Simplex Unscented Kalman Filter (SSUKF), which optimizes the distribution entropy of the innovation based on Akaike information criterion, is proposed. Initially,the algorithm optimizes the distribution entropy of the SSUKF innovation sequences by considering the Akaike information criterion. Subsequently, it constructs a dynamic equation of the sliding window using the residual and innovative sequences based on covariance matching. Furthermore, the algorithm estimates and adjusts the statistical characteristics of the systematic process and measurement noise online and improves the adaptive ability of the SSUKF. The algorithm overcomes the problem of degradation and dispersion of the filtration accuracy of the SSUKF when there are unknown, inaccurate, or uncertain noise statistics. Finally, simulation and integrated navigation of actual tests were performed, respectively. The test outcomes indicate that the proposed algorithm reduces the errors of the east and north velocity by 67.76% and 70.29%, respectively, with RMSE values of 0.1449 and 0.1308m/s. Additionally, when compared to the SSUKF, the proposed algorithm reduces the errors of the latitude and longitude by 56.55% and 81.78%, with RMSE values of 3.1072 and 1.6076m.

In this paper, for the problem of decreased positioning accuracy of Global Navigation Satellite System (GNSS) in urban environment due to signal occlusion, reflection and other factors, combined with the demand of ordinary pedestrians for high-precision positioning, we propose a Pedestrian Dead Reckoning (PDR) combination positioning technique based on GNSS assistance. The double threshold energy detection method is used to detect the MEMS data after wavelet noise reduction, to realize accurate and efficient pace recognition, so as to improve the PDR positioning accuracy, to establish the observation model with the GNSS information, and to correct the PDR algorithm through the improved particle filtering, which can effectively inhibit the cumulative error caused by the long time working of MEMS and the positioning error caused by the reduced accuracy of GNSS in the complex environment. Experiments are designed to demonstrate the algorithm, and the pace recognition rate reaches more than 99% and the mean square error of the positioning result is 1.2m when tested in the complex test section, indicating that the algorithm can effectively improve the accuracy of positioning.

To address the loss of positioning accuracy that occurs when GNSS/SINS integrated navigation operates in urban environments with both non-Gaussian measurement disturbances and mismatched dynamic models, this study introduces a robust filtering framework with an adaptive rate -control mechanism, termed AREM-ISTKF. The method integrates the measurement-side weighting from a Student-t model and the statistically inferred stateside fading factor into a recursive expectation-maximization framework. By adopting a hierarchical architecture, the rate-control layer, driven by the residual energy, allows the filter to rapidly increase its uncertainty estimate when sudden system changes occur, while gradually returning to a stable update mode under normal conditions. This design enables a decoupled yet unified treatment of measurement uncertainty an d dynamic model errors, allowing the filter to maintain reliable positioning accuracy across diverse operating scenarios. Simulations and real-vehicle experiments conducted on a GNSS/INS platform show that the proposed method improves both position and velocity accuracy, particularly under satellite signal interference and highly dynamic vehicle maneuvers.