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

Yanhong Liu,Wen Ye,Bo Wang

doi:10.1109/access.2020.2998719

Yanhong Liu, Wen Ye + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2998719

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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.

Highlights

Acoording to the researches of the past few years, multitask remote sensing loads have gradually become the main trends of aerial remote sensing imaging, such as airborne distributed array antenna SAR and flexible baseline interferometric SAR (InSAR), etc. [1], [2]
One can see from figure that the heading angle convergence rate estimated by variational Bayesian (VB)-central difference Kalman filtering (CDKF) algorithm is faster than the CDKF algorithm and method in [20] and [23]
One can see that the VB adaption can relieve the influence of measurement noise mismatch on state estimation effectively compared with the CDKF algorithm which proceeds under that measurement noise covariance is fixed, performs better than the method in [20] which deals with the measurement noise as t distribution, and method in [23] as the inverse Gamma distribution

Summary

Introduction

Acoording to the researches of the past few years, multitask remote sensing loads have gradually become the main trends of aerial remote sensing imaging, such as airborne distributed array antenna SAR and flexible baseline interferometric SAR (InSAR), etc. [1], [2]. In order to achieve high performance of aerial remote sensing system, it is necessary to obtain high accuracy multi-node motion parameters information of each load’s location. Distributed position and orientation system (POS) is an effective means to present multi-node motion information with high accuracy, which has become one of the key equipment of aerial remote sensing system [3], [4]. (INS)/global positioning system (GPS) [5], and the slave systems are only IMUs. As the typical application mentioned above, the master POS is installed on the middle of the aircraft, the slave IMUs are installed rigidly with the SAR antennas and depend on TA to obtain high accuracy motion parameters of sub-nodes by using accurate motion information of master POS, including position, velocity and attitude, etc. The extended Kalman filter algorithm applies the first-order Taylor-series expansion to linearize the nonlinear problems, which cannot satisfy the accuracy requirements in strong nonlinear

Results

Discussion

Conclusion