He Xiaofeng

High dynamic carrier phase tracking based on adaptive Kalman filtering

The typical GNSS carrier phase tracking loop with fixed parameters is easy to lose lock under high dynamic situation. In this paper the design of two kinds of carrier tracking method is presented. Firstly, the structure of Kalman-based-PLL is introduced. And then a new adaptive method is proposed after analyzing the relationship of carrier phase tracking error and state error covariance characteristics of Kalman-based-PLL. Simulation results show that the adaptive Kalman-based-PLL can track high dynamic carrier phase with 100g/s jerk and the carrier phase tracking error standard deviation is less than 0.011 cycles.

MEMS gyro signal de-noising method based on extended recursive least square

This paper discusses the design of extended recursive least square method based on time series analysis in order to overcome large noise and low precision of MEMS gyro. The method adopts the forgetting factor-based recursive least square which can work well even with uncertain noises. Firstly, ARMA models are used to model gyro random drifts. Secondly, variable forgetting factors enhance the robustness of extended recursive least square approach. Some experiments are carried out and the results show that the proposed method advances the performance of MEMS gyro signal de-noising. It gains better accuracy and better robustness than traditional Kalman filter.

Foot-mounted INS and selected geomagnetic information constraint for individual localization

A pedestrian positioning method aided by magnetic heading is proposed so as to investigate a pedestrian autonomous navigation and positioning method. This method mainly employs the information from an accelerator, gyroscope and Zero Velocity Update (ZUPT) based on gait detection. Besides, geomagnetic information is used to estimate the initial heading and to calibrate the headings during the walk. The result shows that the accuracy of estimated position is effectively improved by fusing the inertial and geomagnetic information. Additionally, this paper presents the cause of magnetic heading error and its solution. By rectangular path test and the figure “8” shape path test, the proposed algorithm is proved to be valid compared with traditional pedestrian positioning method. This method owns a wide engineering application value.

SINS/GPS Tightly Integrated Navigation Algorithm for Land Vehicle Applications

This paper discusses the design of SINS/GPS adaptive Kalman filtering algorithm based on motion constraints for land vehicle applications. The algorithm adopts the architecture of tight integration which can continue to work even with no more than four GPS satellites. The vehicle motion constraints enhance the observation of the integrated navigation when satellite is outage. Adaptive Kalman filter with forgetting factor improves robustness of the integrated system. Some experiments are carried out and the results show that the proposed algorithm advances the performance of SINS/GPS integrated system. It gains better position accuracy 4.86 times and better velocity accuracy 1.23 times than traditional Kalman filtering algorithm.