Airborne Earth Observation Positioning and Orientation by SINS/GPS Integration Using CD R-T-S Smoothing

Abstract

This paper addresses the issue of state estimation in the integration of a Strapdown Inertial Navigation System (SINS) and Global Positioning System (GPS), which is used for airborne earth observation positioning and orientation. For a nonlinear system, especially with large initial attitude errors, the performance of linear estimation approaches will degrade. In this paper a nonlinear error model based on angle errors is built, and a nonlinear estimation algorithm called the Central Difference Rauch-Tung-Striebel (R-T-S) Smoother (CDRTSS) is utilized in SINS/GPS integration post-processing. In this algorithm, the measurements are first processed by the forward Central Difference Kalman filter (CDKF) and then a separate backward smoothing pass is used to obtain the improved solution. The performance of this algorithm is compared with a similar smoother based on an extended Kalman filter known as ERTSS through Monte Carlo simulations and flight tests with a loaded SINS/GPS integrated system. Furthermore, a digital camera was used to verify the precision of practical applications in a check field with numerous reference points. All these validity checks demonstrate that CDRTSS is a better method and the work of this paper will offer a new approach for SINS/GPS integration for Synthetic Aperture Radar (SAR) and other airborne earth observation tasks.

Airborne Position and Orientation System for Aerial Remote Sensing

“…It may be decomposed into two parts that are called forward filtering process and the backward recursion process. After filtering in the forward process, the smoothing algorithm in backward process is used to compute smoothing solutions [36]. Thereinto, the forward process adopts Extended Kalman Filter (EKF) which can be expressed asx…”

“…wherex andx / −1 represent posterior and prior estimates of state x , respectively, P and P / −1 are error covariance matrix of state x , respectively, and K is the gain matrix of EKF. The backward recursion process may adopt R-T-S algorithm which can be expressed as [36]…”

Effect Analysis of GNSS/INS Processing Strategy for Sufficient Utilization of Urban Environment Observations

“…In addition, optimal smoothing based on the Kalman filter can use measurement data to provide the optimal estimate for a certain measurement period. Therefore, when real-time processing is not required, a post-processing smoothing algorithm can make full use of the period observations to improve the pose accuracy [ 24 ]. Currently, commonly used post-processing smoothing algorithms include the Rauch–Tung–Striebel smoothing (RTSS) algorithm and the forward-backward smoothing (FBS) algorithm.…”

An Accurate Motion Compensation for SAR Imagery based on INS/GPS with Dual-filter Correction

“…Hence, Motion Compensation (MOCO) is absolutely necessary for a SAR system. MOCO is mainly achieved in two ways: measurement by motion sensors (Gong and Qin, 2014; Ye et al, 2018b) and estimation from SAR raw data (Chaturvedi et al, 2012; Huang et al, 2016a; 2016b). The former has good real-time performance but cannot completely compensate the motion errors.…”