A proposal of AHRS yaw angle correction with the use of roll angle

Abstract

Purpose Attitude and heading are very important measurements on board aircraft. In modern solutions they are measured by the attitude and heading reference system (AHRS). In some small unmanned systems, the GPS track angle is used for heading corrections instead of the magnetometer; then, the system measures the track angle instead of heading. With a temporary lack of correction signals, the measurement error increases very quickly. Similarly, a quick increase in the measurement error is observed when a magnetic heading sensor used for correction stops working properly. This study aims to propose measurement of the roll angle for yaw angle correction. Design/methodology/approach AHRS algorithms were designed; typical maneuvers were analyzed. The method was verified by simulation and in flight testing analysis. For quantitative analyses, a performance index was proposed. Findings The method enables reduction of the yaw angle error caused by the gyros bias error. This study presents the idea, results of simulations and flight testing data analysis and discusses advantages and limitations of the presented method. Practical implications The presented methodology can be implemented in AHRS systems for manned and unmanned aircraft. Originality/value This study enables more accurate measurement of the yaw angle in the case of missing correction signals.

Analysis of Stochastic Properties of MEMS Accelerometers and Gyroscopes Used in the Miniature Flight Data Recorder

“…The set values are then corrected by removing the constant that occurs when the IMU device is turned on. After integrating the angular velocity ω (or the acceleration a in the case of an accelerometer), AV is determined by (18) [51].…”

“…For correction, two algorithms are usually used. The first one is the Kalman filter [15], and the second one is the complementary filter [16], also used in several other solutions [15,[17][18][19][20]. The Kalman filter minimizes the estimated value variance error, which makes it highly popular.…”