ABSTRACT
Currently, data fusion systems are more familiar with tracking and detection of targets and their real-time applications. It has a past history ranging from traditional individual data collection using related techniques, to an advanced emerging engineering design. Based on the current trend of data fusion real-time tracking applications, this chapter presents a novel data fusion process algorithm using square root information filter. The data fusion process at the decision level is done by merging information from DGPS and wireless sensors for monitoring the train journey in both satellite visible and satellite non-visible environment. The chapter also concentrated on multi-sensor data fusion based on a probabilistic model. In this method, a single network is responsible for gathering information from many sources. The probability model is analyzed using the Bayes theorem.
