Enhanced Disturbance Suppression Method Based on NonlinearH∞Filtering for Distributed POS in Aerial Earth Observation Imaging Application

Abstract

Multinode remote sensing imaging loads have long been a fascinating focus of aerial earth observation applications, where the load denotes the device or equipment located on the aircraft for a specific task, such as aerial camera, scanning laser, and synthetic aperture radar. In order to guarantee high-quality imaging, distributed position and orientation system (POS) serves as high accuracy spatio-temporal reference information for remote sensing loads to realize motion compensation. As a result of the working environment, the flexible structure of the wing will suffer the external disturbance, then for distributed POS, the lever arm between the master system below the abdomen and slave system under the wing is time-varying, also the noise disturbance influences markedly on the motion parameters measurement accuracy. Then aiming at the transfer alignment accuracy improvement of distributed POS, a central difference <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filtering algorithm is proposed, which adopts the central difference Kalman filtering to realize nonlinear state estimation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> filtering to suppress the disturbance influence on estimation, then the high accuracy motion parameters can be obtained. The flight test results show that the developed algorithm can effectively deal with the disturbance influence on transfer alignment estimation and enhance the parameter estimation accuracy, which can be used in aerial earth observation imaging.