RESUMEN
In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE(3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE(3) group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker-Campbell-Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the SE(3) group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional SE(3) group-based model.
RESUMEN
The gravity-aided inertial navigation system is a technique using geophysical information, which has broad application prospects, and the gravity-map-matching algorithm is one of its key technologies. A novel gravity-matching algorithm based on the K-Nearest neighbor is proposed in this paper to enhance the anti-noise capability of the gravity-matching algorithm, improve the accuracy of gravity-aided navigation, and reduce the application threshold of the matching algorithm. This algorithm selects K sample labels by the Euclidean distance between sample datum and measurement, and then creatively determines the weight of each label from its spatial position using the weighted average of labels and the constraint conditions of sailing speed to obtain the continuous navigation results by gravity matching. The simulation experiments of post processing are designed to demonstrate the efficiency. The experimental results show that the algorithm reduces the INS positioning error effectively, and the position error in both longitude and latitude directions is less than 800 m. The computing time can meet the requirements of real-time navigation, and the average running time of the KNN algorithm at each matching point is 5.87s. This algorithm shows better stability and anti-noise capability in the continuously matching process.
RESUMEN
At present, the design and manufacturing technology of mechanically dithered ring laser gyroscope (MDRLG) have matured, the strapdown inertial navigation systems (SINS) with MDRLG have been widely used in military and business scope. When the MDRLG is working, high-frequency dithering is introduced, which will cause the size effect error of the accelerometer. The accelerometer signal has a time delay relative to the system, which will cause the accelerometer time delay error. In this article, in order to solve the above-mentioned problem: (1) we model the size effect error of the mechanically dithering of the MDRLG and perform an error analysis for the size effect error of the mechanically dithering of the MDRLG; (2) we model the time delay error of accelerometer and perform an error analysis for the time delay error of accelerometer; (3) we derive a continuous linear 43-D SINS error model considering the above-mentioned two error parameters and expand the temperature coefficients of accelerometers, inner lever arm error, outer lever arm error parameters to achieve high-precision calibration of SINS. We use the piecewise linear constant system (PWCS) method during the calibration process to prove that all calibration parameters are observable. Finally, the SINS with MDRLG is used in laboratory conditions to test the validity of the calibration method.
RESUMEN
Imperfections in the factors of a rotating accelerometer gravity gradiometer (RAGG), such as accelerometer mounting errors, circuit gain mismatch, accelerometer linear scale factor imbalances, and accelerometer second-order error coefficients, make the RAGG susceptible to its own motion. These motion errors easily cause saturation of the RAGG so that it is unable to work normally. In this study, we propose a scheme for continually adjusting the linear scale factors or mounting angles of the accelerometers to reduce motion sensitivity and for generating a compensation signal based on an analytical model of the RAGG, to compensate motion errors. A numerical model of the RAGG is used to simulate a real imperfect RAGG to allow an online error compensation experiment to be performed. In the experiment, the mean and standard deviation of the air turbulence are 100 mg and 20 mg (1 g = 9.81 m/s2), respectively, and those of the angular velocity are 100 deg/h and 50 deg/h. It takes about 15 min for the RAGG online error compensation system to achieve convergence. In the converged state, the motion noise density of the RAGG is about 1 E/âHz and scale factor balances of the order of 10-9 g/g are maintained. These experimental results suggest that the proposed online error compensation method is valid.
RESUMEN
A moving-base rotating accelerometer gravity gradiometer (RAGG) is an instrument for measuring gravitational gradient signals produced by geological bodies with a certain signal bandwidth. Development and improvement of RAGG requires that they be subjected to testing and calibration; however, the zero-frequency gravitational gradient signals produced by static test masses are not suitable for this purpose. We propose a method in which multiple test masses simultaneously rotating about a RAGG at different angular velocities and in different circular orbits produce the multifrequency gravitational gradient excitation required for testing or calibrating the RAGG. We also present a gravitational gradient extraction method that combines a fore-end circuit design, a multirate filter technique, and a quadrature amplitude modulation demodulation technique. We describe in detail the procedures for gravitational gradient extraction. Multifrequency gravitational gradient excitations are applied to evaluate this extraction method. A RAGG physical simulation system substitutes for an actual RAGG in a multifrequency gravitational gradient extraction experiment. The extracted multifrequency gravitational gradient signal is consistent with theoretical predictions. The gravitational gradient extraction error approximates the noise of the RAGG physical simulation system. These experimental results suggest that the proposed gravitational gradient extraction method is feasible. The research presented in this paper is of great significance for engineering applications.
RESUMEN
The purpose of this study is to calibrate scale factors and equivalent zero biases of a rotating accelerometer gravity gradiometer (RAGG). We calibrate scale factors by determining the relationship between the centrifugal gradient excitation and RAGG response. Compared with calibration by changing the gravitational gradient excitation, this method does not need test masses and is easier to implement. The equivalent zero biases are superpositions of self-gradients and the intrinsic zero biases of the RAGG. A self-gradient is the gravitational gradient produced by surrounding masses, and it correlates well with the RAGG attitude angle. We propose a self-gradient model that includes self-gradients and the intrinsic zero biases of the RAGG. The self-gradient model is a function of the RAGG attitude, and it includes parameters related to surrounding masses. The calibration of equivalent zero biases determines the parameters of the self-gradient model. We provide detailed procedures and mathematical formulations for calibrating scale factors and parameters in the self-gradient model. A RAGG physical simulation system substitutes for the actual RAGG in the calibration and validation experiments. Four point masses simulate four types of surrounding masses producing self-gradients. Validation experiments show that the self-gradients predicted by the self-gradient model are consistent with those from the outputs of the RAGG physical simulation system, suggesting that the presented calibration method is valid.