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The stellar/inertial integrated navigation system, which combines the inertial navigation system (INS) and the star tracker, can restrain the accumulated INS errors. In the traditional loosely coupled stellar/inertial integration method, the star tracker needs to observe more than two navigation stars on an image for attitude determination and to use the attitude information as the observation to estimate the systematic errors of the INS. However, under strong background radiation conditions, the star number in the field of view (FOV) usually drops below 3; thus, the loosely coupled method fails to work. To overcome this difficulty, an improved tightly coupled stellar/inertial integration method based on the observation of the star centroid prediction error (SCPE) is proposed in this paper. It calculates the difference between the extracted star centroid and the predicted star centroid, namely, the SCPE, as the observation and then estimates the INS errors with a Kalman filter. Numerical simulations and ground experiments are conducted to validate the feasibility of the tightly coupled method. It is proved that the proposed method, which makes full use of all star observation information, can improve the navigation accuracy compared with the loosely coupled method and is more robust when there are not enough stars in the FOV. [ABSTRACT FROM AUTHOR]

The purpose of this work was to study bias error in acceleration-based transfer alignment, which is probably caused by cross-correlation between the dynamic lever-arm and the linear motion of a ship. A new prediction model for the cross-correlation-caused error is proposed in this paper. We adopt the Bernoulli-Euler Beam as a simplified ship hull-girder model to verify the existence of the cross-correlation. Then, the mathematical mechanism and the prediction model of the bias error are deduced via the ordinary least squares theory. Three factors influence the bias error in the prediction model: the amplitude of the dynamic lever arm acceleration, the amplitude of the ship motion acceleration, and the cross-correlation between them. Simulation experiments are then conducted to test the influence of the factors. The results show that the mechanism analysis is reasonable, and the bias error prediction model is in agreement with the experimental results. Thus, the proposed prediction model has the potential to deduce the bias error in high-accuracy transfer alignment. [ABSTRACT FROM AUTHOR]

A novel method for dynamic measurement of deflections of the vertical (DOVs) by using the integration of inertial navigation system (INS) and global navigation satellite system (GNSS) has been proposed. Since the attitude errors of INS/GNSS integrated system are directly related to DOV, it is expected that DOV can be also estimated via the observation of INS/GNSS attitude errors. First, an attitude reference decoupled from DOV is constructed with the raw gyroscope data of the INS for INS/GNSS attitude error calculation. Then, DOV and various systematic errors including attitude reference error and inertial sensor errors are accurately modeled. The long-wavelength components of DOV are given by global gravity model, and the short-wavelength components are modeled as derivative second-order Gauss–Markov processes. Finally, DOV and systematic errors are simultaneously estimated by a Kalman smoother with the observation of INS/GNSS attitude error. A ship-borne survey campaign is conducted to validate the feasibility of the proposed method. Four sets of DOV data were obtained by two INS/GNSS integrated navigation systems along with two repeated survey lines. The experimental results show that the internal accuracy of the four sets of DOV data can reach better than 0.35″(1σ). When wavenumber correlation filter (WCF) algorithm is applied to the DOV data obtained by each system along the repeated lines, the DOV estimates from the two systems show good consistency, and the internal accuracy can be improved to about 0.2″(1σ). [ABSTRACT FROM AUTHOR]