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.

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.

Location fingerprinting in wireless LAN positioning has received much attention recently. One of the key issues of this technique is generating the database of fingerprints. The conventional method does not utilise the spatial correlation of measurements sampled at adjacent reference points, and the training process is not an easy task. A new method based on kriging is presented which can not only achieve more accurate estimation, but can also greatly reduce the workload and save training time. This can make the fingerprinting technique more flexible and easier to implement.