Jin Zhi-hua

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.

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.