Sensor fusion using Dempster-Shafer theory II: static weighting and Kalman filter-like dynamic weighting

Context sensing for context-aware HCI challenges traditional sensor fusion methods with its requirements for (1) adaptability to a constantly changing sensor suite and (2) sensing quality commensurate with human perception. We build this paper on two IMTC2002 papers, where the Dempster-Shafer "theory of evidence" was shown to be a practical approach to implementing the sensor fusion system architecture. The implementation example involved fusing video and audio sensors to find and track a meeting participant's focus-of-attention. An extended Dempster-Shafer approach, incorporating weights representative of sensor precision, was newly suggested. In the present paper we examine the weighting mechanism in more detail; especially as the key point of this paper, we further extend the weighting idea by allowing the sensor-reliability-based weights to change over time. We will show that our novel idea - in a manner resembling Kalman filtering remnance effects that allow the weights to evolve in response to the evolution of dynamic factors can improve sensor fusion accuracy as well as better handle the evolving environments in which the system operates.