There are two options for the state estimation procedure by the integration filter for the integrated SINS/GPS, namely: direct navigation mode and indirect navigation mode. Determining which one of the two navigation modes will be more celebrated than the other under different conditions is an important but so far less seriously treated issue. The two navigation modes were systematically studied in terms of the system model, filtering algorithm and application issues. The car-mounted experiments were carried out to compare two navigation modes under different road conditions. The results show that direct navigation mode performs indirect navigation mode in terms of accuracy and stability. However, indirect navigation mode has low calculation burden and simple filter design.

Twisted bilayer (tB) transition metal dichalcogenide (TMD) structures formed by two pieces of a periodic pattern overlaid with a relative twist manifest novel electronic and optical properties and correlated electronic...

Aiming at the problem of modeling and compensation of the fiber optic gyroscope (FOG) drift caused by temperature, a novel compensation method for FOG temperature drift based on transformed unscented Kalman filter (TUKF) is proposed. Elman network with faster convergence speed is used to modeling and TUKF algorithm is adopted to train the weights of Elman network, which effectively solves the problem of numerical instability. The results prove that the proposed method has higher precision compared with Elman network and IUKF network models. By using the TUKF algorithm, the root mean square errors (RMSE) are improved by 60%  in temperature rise period and 50.5% in fall period.

The cascade compensation algorithm for no damp strapdown inertial navigation system (SINS) is designed based on control model and error characteristic of SINS level-loop. According to stability and frequency characteristic analysis for SINS level-loop, a compensation network, which is chosen to alter either the roots location or the frequency response of the system, is cascaded with the SINS level-loop to damp the oscillatory or accumulated errors. The network parameters are attained by designing the frequency characteristic using frequency domain performance indexes, which are transformed from the expected time domain indexes. The algorithm implementation of the compensator is competed by solving the first-order differential equations set, which is derived from Laplace transform of the compensation network using intermediate variables. Results of digital simulation and test on sea proved that the system attitude accuracy improved 1 order and the system position accuracy improved 1 time when the compensation network was cascaded into SINS.