Classification of Vibrating Systems (CLOVIS)

Added: Mar 29, 2017 12:58 pm The objective of the CLOVIS effort is to develop, train, and assess extensible algorithms that can classify and identify mechanically operating systems utilizing measurements from an airborne LiDAR vibrometer. The overall goal is to develop a near real-time (i.e. identification result is available <1 second after data is available) suite of robust algorithms that can be efficiently trained to classify and identify a number of mechanical systems for the purpose of demonstrating the combat identification capability of airborne vibration sensors. The trained algorithms and their associated parameters must be suitable for executing on the current target processor and operating system dedicated for software demonstrations in the LiDAR sensor (ORION-7654 Single Board Computer with a Linux operating system). Data for training the algorithms shall consist of measurements from an airborne LiDAR system and truth data from accelerometers and tachometers collected from targets of interest. The initial number of targets will be limited to five or six but the algorithms and their associated identification and classification parameters must be extensible to 30 or 40 targets (with solution time <1 second). The algorithms must be able to correctly identify a particular class of vehicle greater than 95% with a confidence level of 90%.