An Improved Real-Time Transfer Alignment Algorithm Based on Adaptive Noise Estimation for Distributed POS

Abstract

Distributed position and orientation system (POS) plays an important role in the fields of aerial remote sensing, which serves the sensors by precise motion information. For distributed POS, the slave systems consist of low accuracy inertial sensors, which must depend on the high accuracy motion information of the master to proceed transfer alignment (TA) to improve accuracy. Generally, the TA filtering algorithms perform superior performance when the noise statistical characteristic is known and accurate, however like gust, engine vibration and other external disturbances both will cause the inertial sensors output with unknown, varying noises and performance decline. Aiming at this, a variational Bayesian central difference Kalman filtering algorithm for distributed POS real-time TA is developed to suppress the effect of external noise, incorporating the central difference Kalman filtering (CDKF) algorithm and variational Bayesian (VB) adaption theory. In detail, the algorithm is to apply the noise estimation by VB adaption to the CDKF algorithm to achieve real-time TA accuracy enhancement. Distributed POS flight test is devoted for the algorithm validation, by the comparison, evident progress in TA accuracy has been presented.