Electronic Version

Although the Kalman filter (KF) is widely used in practice, its estimated results are optimal only when the system model is linear and the noise characteristics of the system are already exactly known. However, it is extremely difficult to satisfy such requirement since the uncertainty caused by the inertial instrument and the external environment, for instance, in the aided inertial navigation. In practice almost all of the systems are nonlinear. So the nonlinear filter and the adaptive filter should be considered together. To improve the filter accuracy, a novel adaptive filter based on the nonlinear Cubature Kalman filter (CKF) and the Variance-Covariance Components Estimation (VCE) was proposed in this paper. Here, the CKF was used to solve the nonlinear issue while the VCE method was used for the noise covariance matrix of the nonlinear system real-time estimation. The simulation and experiment results showed that better estimated states can be obtained with this proposed adaptive filter based on the CKF.

A major error component of Global Positioning System (GPS) is the ionospheric delay. Ionopspheric error can be reduced by a dual frequency receiver using a linear combination technique that can not be applied with a single frequecy receiver. However, an accurate ionospheric error modeling for single-frequency receiver is required. Due to the nonlinearity of the ionospheric error, a highly nonlinear wavelet network (WN) method is proposed in this paper. The main objective of the paper is to develop a short-term prediction model based on a short dataset. Therefore, five GPS stations with five days of ionospheric datasets along with time and location were employed to develop the proposed WN-based ionospheric model. Four days of datasets were employed to develop the model and one day of dataset was employed to test the prediction accuracy. To validate the WN-based ionospheric model, a comparison was made between the developed WN-based ionospheric model and the CODE, JPL and IGS Global Ionospheric Map (GIM) models. It is shown that the Root-Mean-Squared (RMS) errors of the developed WN-based ionospheric model are 2.51 TECU, 2.75 TECU and 2.50 TECU (Total Electronic Content Unit) with percentage errors of about 3.4%, 3.8% and 3.4% when compared with the CODE, JPL and IGS GIM models.

WiFi and Bluetooth are two most commonly used short range wireless communication technologies. Recent years, with increasing number of WiFi and Bluetooth mobile terminals, tags, and other devices, a demand for integration and coexistence of these two technologies including their positioning function is booming. In this paper, we firstly investigate the interferences between WiFi and Bluetooth signals from the signal and protocol perspectives. Secondly, the principle of fingerprinting approach for WiFi positioning is introduced. In order to evaluate the performance of WiFi fingerprinting coexisted with Bluetooth, both occurrence-based and Weibull-based approaches are utilized for generating the database. Field tests present the interference in the WiFi and Bluetooth coexistence environments. A WiFi mobile device with a Bluetooth device nearby obtains poor positioning results due to the interference. Weibull-based database has more robust performance than occurrence-based database in the coexistence environments.

Integer ambiguity resolution of carrier-phase measurements from a single receiver can be implemented by applying additional satellite corrections (products) to mitigate unmodelled satellite equipment delays. Interoperability of different PPP-AR products would allow the PPP user to transform independently generated PPP-AR products to obtain multiple fixed solutions of comparable precision and accuracy with limited changes required to user PPP measurement processing software. The ability to provide multiple solutions would increase the reliability of the solution for, e.g., real-time processing; if there were an outage in the generation of one set of PPP-AR products, the user could instantly switch streams to a different provider.

There are currently three main public providers of real-time products that enable PPP-AR. These include School of Geodesy and Geomatics at Wuhan University (SGG-WHU), Natural Resources Canada (NRCan) and Centre National d’Etudes Spatiales (CNES). The presented research examines the PPP-AR products generated from the FCB (Fractional Cycle Bias) model and IRC (Integer Recovery Clock) model that have been transformed into the DC (Decoupled Clock) format and applied within the PPP user solution. Interoperability of the different PPP-AR products is a challenging task due to the public availability of different quality of products, limited literature documenting the conventions adopted within the network solution of the providers and unclear definitions of the corrections. The novelty of the research is in the analysis of using the transformed products. The convergence time (time to first fix and time to a pre-defined performance level), position precision (repeatability), position accuracy and solution outliers are examined. Equivalent performance was noted utilizing the different methods. Of the four solutions, FCB products had the highest accuracy. This is attributed to the products being generated using final IGS orbit and clock products. To confirm this, FCBs generated using GRG orbit and clock products were also examined and comparable performance was observed between the FCBs and IRC (GRG) products. The least accurate solution was obtained using the IRC (CNT) products, which was due to the products being archived real time products.