Low dynamic IMU alignment

IMU alignment is addressed by examining results from two different INS error modeling approaches using actual flight test data recorded from captive pod mounted IMU and reference INU. The emphasis of this paper is alignment during the aircraft´s low dynamics ground operations before takeoff. The two MS error models used to produce the alignment results are based on computer frame and perturbation error models, and are formulated to use position observations in a transfer alignment Kalman filter algorithm. The use of position observations is consistent with other navigation aides such as GPS. Performance measures used to evaluate the two INS error models´ results include: filter state convergence rates, IMU navigation solution heading compared to the reference MU heading, and post-alignment unaided navigation position error growth during manoeuvres after aircraft takeoff. For all three, the perturbation error model shows better results. The results suggest that alignment and aided navigation can be accomplished and sustained with a navigation filter algorithm which implements the perturbation error model without an alignment manoeuvre