Multi-task remote sensing sensors have become attractive development directions of aerial remote sensing system, which rely on distributed position and orientation system (DPOS) to provide multi-node motion parameters to achieve superior performance. DPOS depends on transfer alignment from its master system to slave inertial measurement units to obtain multi-node motion information. However, for DPOS, there are many factors like carrier maneuver mode and external disturbance which will result in time-varying measurement noise and further degrade transfer alignment performance of DPOS obviously. In this work, a transfer alignment method based on variational Bayesian cubature RTS smoothing is developed to improve the accuracy of DPOS, which is implemented by combining the cubature RTS smoothing algorithm and variational Bayesian estimation method to deal with the time-varying measurement noise. A semi-physical simulation based on real flight experiment has been conducted, the results show that the motion parameter accuracy has achieved noticeable enhancement than the existing cubature RTS smoothing algorithm.

Considering two disadvantages in traditional gravity matching aided inertial navigation system, low matching accuracy and error accumulation, we propose an improved gravity matching algorithm and aided method for inertial navigation system. Instead of using the sequence sampling, the single point sampling is applied to improve the structure of proposed algorithm, enhancing the matching speed and efficiency. In the aided navigation system method, we use combination of Sage-Husa adaptive filter and strong-tracked Kalman filter to make further optimal estimation of the matching trajectory. Simulation results show the effectiveness of the real-time ICCP algorithm and the combined filter algorithm. Comparing to the traditional methods, proposed method provides higher matching and navigation accuracy.

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.

Airborne position and orientation system (POS) can provide high accuracy motion parameters for multiple aerial mission loads and has became a vital equipment in aerial remote sensing system. However, the airborne POS often suffers from the systematic kinematic model and observation model inaccuracy, which further affects the accuracy of motion parameters, and the conventional innovation-based adaptive method only deals with observation model inaccuracy and results in limited performance improvement. In this work, a dual adaptive factors filtering algorithm is adopted to deal with the kinematic model and observation model inaccuracy together, which has been validated by the real flight experiment and the results show that performance improvement has achieved.

A distributed position and orientation system (DPOS) can provide abundant motion parameters for multi-task remote sensing loads to conduct its motion compensation. The motion parameters of imaging loads equipped with slave Inertial Measurement Units (IMUs) can be obtained by transfer alignment from master system to slave IMU. However, considering the volume, weight, and cost of the slave IMU, it is impossible to install IMUs near all the remote sensing loads in engineering practice. Aiming at the problem, a global estimation method based on spatial-temporal Kalman filter (STKF) for DPOS is proposed to estimate the motion parameters of remote sensing loads not equipped with slave IMUs. Combining kriging and Kalman filter, and taking into account the spatial-temporal correlation between points, spatial-temporal Kalman filter can make full use of the useful information of the data and enable us to obtain the optimal estimation in time and space. In order to evaluate the effectiveness of the proposed method, the semi-physical simulation based on flight experiment is conducted. The results show that the proposed method not only can realize the global estimation, but also provides us some new insights into the layout scheme of DPOS.

For distributed Position and Orientation System (POS), multi-node motion information are measured mainly by transfer alignment technology. The KF-based non-linear filtering algorithm provides optimal integrated solution for transfer alignment. However, on account of uncertain external environment and flexible lever arm compensation error, the noises of transfer alignment exhibit non-Gaussian property and further cause lower accuracy of distributed POS. In this paper, Correntropy-based CDKF is proposed by combining central difference Kalman filter (CDKF) and maximum correntropy criterion (MCC), which aims to decrease the effect of non-Gaussian noises. We rebuild a linear regression model and define a cost function under MCC to modify measurement information, and finally accurate state estimation is achieved. Meanwhile, the error model of nonlinear transfer alignment is formulated where flexible deformation and lever arm effect are considered, and the attitude and velocity information of main POS are transferred to slave IMU to improve its performance. Based on a semi-physical simulation, the performance of proposed method is proved to outperform the standard CDKF in term of estimation accuracy.