The position and orientation system is integrated measurement equipment for the airborne remote sensing system, and the measurement precision plays a vital role in imaging compensation. Because of the observable degree of state variable changes in real time with movement, the existing filtering method with constant state parameters reduces the attitude precision. In order to solve the problem, a state parameter adjustment filtering method based on instantaneous observable degree model is proposed. An instantaneous observable degree model is established to analyze the impact mechanism of movement on observable degree. On the basis of the model, an observable adjustment factor is constructed to adjust the state parameter. Flight experiment is carried out, which verifies that the proposed method is effective in improving the attitude precision.

The distributed position and orientation measurement system (DPOS) depends on transfer alignment to accurately measure multi-node motion information for multi-task imaging sensors. However, the transfer alignment accuracy is limited by the flexible baseline accuracy between nodes. A flexible baseline measurement method based on the multiple camera laser-rangefinder measurement system (MCLS) is proposed in this paper. The relative position and orientation information between the nodes are measured directly by using MCLS. Combined with the advantages of visual measurement and laser ranging, this method completes the non-contact measurement and overcomes the difficult problem of the poor precision of the flexible baseline caused by inaccurate mathematical modeling. In order to verify this method, a distributed flexible baseline simulation platform is built and static test experiments are carried out. It shows that this method can realize sub-millimeter level measurement. At the same time, it can provide a strong support for the application of DPOS.

With the demand for high resolution remote sensing, load array technology has gradually become an effective measure to improve imaging resolution. However, the external flow and internal engine vibration disturbance may lead to the flexible deformation of wings. The traditional rigid baseline error compensation method cannot solve the problem of serious coupling movement error caused by flexible deformation. To address the problem, a transfer alignment model based on fiber Bragg grating for distributed position and orientation system is proposed in this paper. Firstly, based on the multidimensional requirements of flexible deformation information, the layout scheme of fiber Bragg grating was designed, then the continuous strain in the wing surface was obtained after the quadratic fitting of strain measured by fiber Bragg gratings, and the deformation displacement and angle are calculated. Thirdly, flexible deformation compensation for distributed position and orientation system based on fiber Bragg grating was studied. The state equation including position error, velocity error, misalignment angle, and inertial device error was established. The position and attitude information compensated by the flexible lever arm was used as the quantitative measurement. The filtering estimation improved the measurement accuracy of the slave inertial navigation systems. At last, the experiment was carried out and showed that the accuracy of the transfer alignment has been improved significantly.

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.

Attitude error models play an important role in analyzing the characteristics of navigation error propagation for the design and operation of strapdown inertial navigation systems (SINS). However, the majority of existing attitude error models focus on misalignment, rather than Euler angle errors. Misalignment cannot directly describe attitude error propagation, which is an indirect measurement. To solve the problem, a general Euler angle error model of SINS is proposed. Based on Euler angle error propagation analysis, relative Euler angle errors, and convected Euler angle errors are introduced to compose the general Euler angle error model. Simulation experiments are carried out to verify the proposed model.

Position and orientation system (POS) is widely applied in remote sensing applications, which provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. As a multisensory system, time-delay usually occurs during signal preprocessing, transmission, integration, filtering, etc., which decrease the measurement accuracy. As the POS motion parameter varies nonlinearly, the time-delay error can't be precisely compensated by the conventional linear method. To solve this problem, a novel time-delay error compensation method based on Support Vector Machine (SVM) is proposed, and Particle Swarm Optimization (PSO) is used to optimize the key parameters of SVM. In verifying the proposed method, a flight experiment was conducted. Experimental results show that the proposed method can effectively compensate the time-delay error of POS, and is more accurate than the conventional one.