This is useful, for example, in determining the attitude of an aircraft using low-cost sensors. Another example is using the data fusion approach to determine the traffic state (low traffic, traffic jam, medium flow) using road side collected acoustic, image and sensor data. In the field of autonomous driving, sensor fusion is used to combine the redundant information from complementary sensors in order to obtain a more accurate and reliable representation of the environment. Although technically not a dedicated sensor fusion method, modern Convolutional neural network based methods can simultaneously process very many channels of sensor data (such as Hyperspectral imaging with hundreds of bands ) and fuse relevant information to produce classification results. See also Brooks – Iyengar algorithm Data (computing) Data mining Fisher's method for combining independent tests of significance Image fusion Multimodal integration Sensor grid Transducer Markup Language (TML) is an XML based markup language which enables sensor fusion. References External links Discriminant Correlation Analysis (DCA) International Society of Information Fusion Robotic sensing Computer data Sensors