Tian Wei-feng

A novel method improving the alignment accuracy of a strapdown inertial navigation system on a stationary base

In the process of initial alignment for a strapdown inertial navigation system (SINS) on a stationary base, the east gyro drift rate is an important factor affecting the alignment accuracy of the azimuth misalignment angle. When the Kalman filtering algorithm is adopted in initial alignment, as the east gyro drift rate cannot be estimated, it yields a constant error in the estimation of the azimuth misalignment angle. In this paper, a novel method is proposed to improve the alignment accuracy. The update equation for the estimate of the east gyro drift rate is established, and the estimation accuracy of the azimuth misalignment angle has been improved greatly through adjusting the estimated value of the east gyro drift rate on the basis of the update equation. Simulation results show that the method proposed in this paper is efficient in initial alignment for a medium accuracy SINS on a stationary base.

IAE-adaptive Kalman filter for INS/GPS integrated navigation system

Abstract A marine INS/GPS adaptive navigation system is presented. GPS with two antenna providing vessels altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

A projection pursuit learning network, for modeling temperature drift of FOG

The large temperature drift caused by variation of environmental temperature is the main factor affecting the performance of fiber optical gyroscope (FOG). Considering the fact that the temperature drift is a group of multi-variable nonlinear time series related with temperature, a new method named projection pursuit learning network (PPLN) is employed in this paper to model the temperature drift of FOG. The PPLN integrates the advantages of artificial neural network (ANN) and projection pursuit algorithm (PP), and is capable of providing less network neurons and good robustness. Numerical results from measured temperature drift data of FOG verify the effectiveness of the proposed method, and good predication of independent tested data is obtained.