Weidong (John) Ding

PhD student since mid-2004
Phone: +61 2 9385 4185
Fax: +61 2 9313 7493
Room Number: EE Building 402
Email:weidong.ding@student.unsw.edu.au

B.E. in Electrical Engineering, Beijing Polytechnic University, P.R. China, 1992.
M.E. in Electrical Engineering, University of New South Wales, Sydney, Australia, 2004.

1994 to 2002, Electrical Engineer and Project Manager, VA TECH Beijing Ltd., Beijing, P.R. China.
1992 to 1994, Assistant Lecturer, Beijing Polytechnic University, Beijing, P.R. China.

Experience / Skills:

• Specialised in industrial automation and control technology, including embedded systems, PLC, DCS, SCADA and industrial instrumentations.
• Engineering with automation and protection systems of 6 hydro power plants and 8 substations.
• Project coordination and management.

Computer Expertise:

• MS DOS and Windows operating systems, and MS office applications.
• Knowledge of software engineering and network management.
• Programming with Pascal, C, Delphi, MATLAB, etc.
• Familiar with Protel, AutoCAD and Ectrl, etc.
• Circuit design and programming of microprocessor embedded systems.

Research Project:

Integrating GPS/INS for Positioning and Geo-Referencing: System Design, Development and Testing

Due to the growing awareness of the spatial information, increasing demands from surveying and mapping, navigation, GIS, and LBS industries require building up of a variety of spatial infrastructures. GNSS represents the major break-through technology during the last three decades in this field. With GNSS, positioning at any where in any time on the Earth becomes tangible. However, the present GNSS applications are still suffering from some limitations like sensitivity to signal blockage, lower updating rates, multi-path error etc.. Most importantly, the present GNSS can still not provide a sufficient attitude solution for real time navigation and geo-referencing applications. These issues are currently being addressed with multi-sensor integration solutions. INS is often picked up as the premier integration sensor due to its self-sufficient nature and compensatory characteristic when compared with GNSS.

My present research is focusing on the investigation of several challenging issues in designing a generic GPS/INS integration platform for positioning and geo-referencing applications. To this end, three aspects of research have been carrying out:

(1) Time synchronization Having precise time synchronisation is critical for achieving high data fusion performance. In this part of research, the limitations and advantages of various time synchronisation scenarios and existing solutions have been investigated. A criterion for evaluating synchronisation accuracy requirements has been derived based on the comparison of the Kalman filter innovation series and the platform dynamics. A new time synchronisation solution has been proposed and tested using real field data.

(2) Adaptive filtering The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of those modelling errors. Starting from the covariance matching algorithms, the online stochastic modelling algorithm has been further investigated and a new adaptive process noise scaling algorithm has been proposed and verified using real field data.

(3) INS error calibration using stand-alone GPS INS manufactured using MEMS technology is quickly substituting the role of conventional INS. However, the performance of MEMS INS is still suffering from large errors including significant scale factor nonlinearities, misalignment, high noise, and temperature varying biases. Precisely modeling and compensating for these errors present significant challenges from several research aspects. Together with it, calibration of MEMS INS errors in using stand-alone GPS, which is targeted for real-time applications, is even more challenging. To improve the calibration and integration performance, a new velocity aiding method and a new integration structure have been proposed and tested using real field data.

Interests:

Soccer, Table Tennis.