Despite significant progress in GPS/INS integration-based direct geo-referencing (DGR) technology over the past decade, its performance still needs to be improved in terms of accuracy and tolerance to GPS outages. This is mainly due to the limited geometric strength of the GPS satellite constellation, the quality of INS and the system integration technology. This research is focused on pseudolite (PL) augmentation to enhance the geometric strength of the GPS satellite constellation, and the Neural Network (NN) aided Kalman filter (KF) system integration algorithm to improve the geo-referencing system's performance during GPS outages. The main research contributions are summarized as below: a) Systematic errors introduced by pseudolites have been investigated. Theoretical and numerical analyses reveal that errors of troposphere delay modelling, differential nonlinearity and pseudolite location are sensitive to pseudolite receiver geometry. Their effect on final positioning solutions can be minimised by selecting optimal pseudolite and receiver locations, which is referred to as geometry design. Optimal geometry design for pseudolite augmented systems has been proposed based on simulation results in airborne surveying scenarios. b) Nonlinear geometry bias, or nonlinearity, exists in single difference processes when the unit vectors from the reference and user receivers to a satellite or pseudolite are non-parallel. Similar to long baseline differential GPS (DGPS), nonlinearity is a serious issue in pseudolite augmentation. A Projected Single Difference (PSD) method has been introduced to eliminate nonlinear geometry bias. An optimized expression has been derived to calculate the direction of project vectors, and the advantages of applying PSD in pseudolite augmented airborne DGPS have been demonstrated. c) A new method for pseudolite tropospheric delay modelling has been proposed, which is based on single-differenced GPS tropospheric delay models. The performance of different models has been investigated through simulations and field testing. The advantages and limitations of each method have been analysed. It is determined that the Bouska model performs relatively well in all ranges and elevations if the meteorological parameters in the models can be accurately collected. d) An adaptive pseudolite tropospheric delay modelling method has been developed to reduce modelling error by estimating meteorological parameters in real-time, using GPS and pseudolite measurements. Test results show that pseudolite tropospheric delay modelling errors can be effectively mitigated by the proposed method. e) A novel geo-referencing system based on GPS/PL/INS integration has been developed as an alternative to existing GPS/INS systems. With the inclusion of pseudolite signals to enhance availability and geometry strength of GPS signals, the continuity and precision of the GPS/INS system can be significantly improved. Flight trials have been conducted to evaluate the system performance for airborne mapping. The results show that the accuracy and reliability of the geo-referenced solution can be improved with the deployment of one or more pseudolites. f) Two KF and NN hybrid methods have been proposed to improve geo-referenced results during GPS outages. As the KF prediction diverges without measurement update, the performance of a GPS/INS integrated system degrades rapidly during GPS outages. Neural networks can overcome this limitation of KF. The first method uses NN to map vehicle manoeuvres with KF measurement in a loosely coupled GPS/INS system. In the second method, an NN is trained to map INS measurements with selected KF error states in a tightly coupled GPS/INS system when GPS signals are available. These training results can be used to modify KF time updates. Optimal input/output and NN structure have been investigated. Field tests show that the proposed hybrid methods can dramatically improve geo-referenced solutions during GPS outages.
| Identifer | oai:union.ndltd.org:ADTP/230362 |
| Date | January 2007 |
| Creators | Wang, Jianguo Jack, Surveying & Spatial Information Systems, Faculty of Engineering, UNSW |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
Page generated in 0.0015 seconds