Lecture Notes: Estimation and Sensor Fusion

This note discusses multi-sensor fusion. Through sensor fusion we may combine readings from different sensors, remove inconsistencies and combine the information into one coherent structure. This kind of processing is a fundamental feature of all animal and human navigation, where multiple information sources such as vision, hearing and balance are combined to determine position and plan a path to a goal. While the concept of data fusion is not new, the emergence of new sensors, advanced processing techniques, and improved processing hardware make real-time fusion of data increasingly possible. Despite advances in electronic components, however, developing data processing applications such as automatic guidance systems has proved difficult. Systems that are in direct contact and interact with the real world, require reliable and accurate information about their environment. This information is acquired using sensors, that is devices that collect data about the world around them. The ability of one isolated device to provide accurate reliable data of its environment is extremely limited as the environment is usually not very well defined in addition to sensors generally not being a very reliable interface. Sensor fusion seeks to overcome the drawbacks of current sensor technology by combining information from many independent sources of limited accuracy and reliability to give information of better accuracy and reliability. This makes the system less vulnerable to failures of a single component and generally provide more accurate information. In addition several readings from the same sensor are combined, making the system less sensitive to noise and anomalous observations