Data-Driven Classification of Ch-146 Manoeuvres Using Mems-Imu Sensor System

Abstract

This paper describes the use of a low-cost standalone MEMS-IMU (micro-electromechanical system - inertial measurement unit) sensor system developed by the National Research Council Canada (NRC) for manoeuvre recognition in helicopters. The system records accelerations, angular rotation rates, magnetic flux, altitude, location and velocity through its IMU and GPS. The MEMS-IMU system was flown on the Bell 412 CH-146 Griffon helicopter in a series of scripted flights consisting of 60 manoeuvres and regimes from the helicopter's usage spectrum. A flight log recorded by passengers with detailed start and stop times and identification of the manoeuvres during flight was key information in the development of manoeuvre recognition models. Statistical tests to analyze the data diversity between each flight showed significant variability of the parameters from flight to flight. Because of this variability and because there was significant imbalance in the distribution of data for the individual manoeuvres, a pooled stratified sampling scheme was used to construct a representative training set for developing data-driven models. Different subsets of the MEMSIMU measurements were explored to exclude GPS and/or magnetometer readings. Even with the different subsets, the classifier results using stratified sampling show that very high overall classification accuracy can be obtained using the measurements from the standalone MEMS-IMU sensor system.