The CyberCraft initiative is designing a system of agents to provide command and control of future air force information systems. In such a distributed environment, trust between agents becomes pertinent to provide a combatant commander an accurate picture of the network by merging conflicting data and giving the commander confidence in the control of their network. The trust vector model breaks from traditional trust models as it provides a range of trust values for data, enabling the system to describe partial trust in data rather than binary values of trust or no trust, and from that build confidence intervals in fused data. The trust vector model builds trust based on current and historical data, but does not address the limits of utility of the historical data. This research provides implementors of the trust vector model input as to how much historical data should be stored to balance accuracy of trust with limits of storage

This paper presents an analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKF-SLAM). We show that the algorithm produces very optimistic estimates once the "true" uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, in general, be detected without ground-truth, although a very inconsistent filter may exhibit observable symptoms, such as disproportionately large jumps in the vehicle pose update. Conventional solutions - adding stabilising noise, using an iterated EKF or unscented filter, etc., - do not improve the situation. However, if "small" heading uncertainty is maintained, EKF-SLAM exhibits consistent behaviour over an extended time-period. Although the uncertainty estimate slowly becomes optimistic, inconsistency can be mitigated indefinitely by applying tactics such as batch updates or stabilising noise. The manageable degradation of small heading variance SLAM indicates the efficacy of submap methods for large-scale maps

This paper presents the results of an experimental program for evaluating sensors and sensing technologies in an underground mining applications. The objective of the experiments is to infer what combinations of sensors will provide reliable navigation systems for autonomous vehicles operating in a harsh underground environment. Results from a wide range of sensors are presented and analysed. A conclusion as to the best combination of sensors is drawn.

A novel Kalman filter based sonar navigation system is presented that utilises directed sensing techniques to achieve continuous indoor localisation of a mobile robot. Data from extensive trials is summarised demonstrating continuous, reliable, localisation using this system in a wide variety of active industrial sites. Full details of experimental method and performance assessment are given.

With the use of a scanning optical ranger, a dense map of an environment has been constructed. From this, line segment targets are extracted and matched against an a priori map to obtain observations for an extended Kalman filter. This filter is then able to update predictions of an autonomous guided vehicle's position (made using odometry) in real time, offering high-speed position estimation using inexpensive sensing techniques.