Weidong Ding

Improving Adaptive Kalman Estimation in GPS/INS Integration

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. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

Precise Velocity Estimation with a Stand-Alone GPS Receiver

A scrutiny of the existing time differencing carrier phase (TDCP) velocity estimation algorithms has revealed several shortcomings that could be further improved. One of these is that the velocity estimation at epoch t would require the receiver positions at epoch t +Δ t or more are available. Another is the usage of satellite velocities in the calculations which would increase the receiver computational load and degrade the accuracy. In this paper, an improved TDCP velocity estimation approach has been proposed and tested. The new approach depends only on receiver position at epoch t and satellite positions at epoch t and t +Δ t . Satellite velocities are not required in this calculation. This proposed algorithm has been validated using static and kinematic field test data, showing that equivalent velocity accuracy achievable by using differential GPS techniques can be made possible with the proposed standalone GPS method.

Indoor positioning within a single camera and 3D maps

In this paper, we propose a method of vision-based positioning with the use of single camera and newly defined 3D maps for indoor localization and navigation purposes. Our work here is to address the accuracy and reliability concerns of an indoor navigation system. The main contribution will be the adoption of photogrammetric 6DOF pose estimation method to improve the positioning accuracy. DOPs are introduced to evaluate positioning precision within vision-based domain. Quality control strategies are also applied to detect outliers in the observation and strengthen system reliability. Besides, only natural landmarks are required in the proposed method to provide absolute position and orientation information.

Optimising Fault Detection and Exclusion in positioning

Reliability and integrity has become increasingly important in ubiquitous positioning systems, and particularly in critical applications. To implement reliability and integrity measures, Fault Detection and Exclusion (FDE) procedures should be used. However, the current FDE procedures in the chi-square test and the outlier test are suboptimal in terms of continuity and availability. This is due to the assumption that the bias always corresponds with the measurement that has the largest Protection Level (PL), and in the setting of the continuity probability to a predefined value rather than being maximised.