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Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aimin...
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This contribution presents a constraints-based loosely-coupled Augmented Implicit Kalman Filter approach to vision-aided inertial navigation that uses epipolar constraints as output map. The proposed approach is capable of estimating the standard navigation output (velocity, position and attitude) together with inertial sensor biases. An observabil...
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... Fast initial alignment methods for SINS based on the saved data and iterative calculation have been researched a lot in recent years [17,[22][23][24][25]. e iterative process could be divided into forward-forward process and forward-backward process, and then the normal fine alignment algorithm is combined with the iterative process. ...
... where the superscript " ← " denotes the backward process [23,25]. e backward navigation process is just like the playback of the forward process. ...
Iterative algorithms based on saved data have been researched for strapdown inertial navigation system to make the best use of sensor data, and different alignment algorithms can be carried out during the iterative process to fulfil initial alignment. This paper firstly analysed two interactive processes. The forward-backward process needs to change the normal system model in the backward process and have additional error factors. While the forward-forward process is easy to be carried out without changing the system model, the transition error will also decrease the accuracy. In addition, traditional iterative methods always need to save a large amount of data and do redundant computation, which is not highly effective, especially in view of the real-time property. To address these effects, a forward-forward solution in the inertial frame based on Kalman filter is analysed. This method calculates the attitude transfer matrix in the inertial frame to deal with angular disturbances and avoids saving all the original measurement data during the process, which makes the method more stable and faster to fulfil in the real-time system. Simulation and turntable test validates the performance of the proposed method.
... In order to fulfill the requirements of high accuracy and short time for initial alignment, the same set of sensor data can be used repeatedly to conduct initial alignment. The SINS mathematical platform is adjusted accordingly with updated compensation parameters [22], and a fast compass alignment is feasible based on iterative calculation [23]. The forward and backward processes can be effectively applied in INS alignment, and the analysis of forward and backward compass processes has been carried out using the error equations in frequency domain [24]. ...
For the strapdown inertial navigation system (SINS), the procedure of initial alignment is a necessity before the navigation can commence. On the quasi-stationary base, the self-alignment can be fulfilled with the high quality inertial sensors, and the fine alignment is usually executed to improve the alignment performance. Generally, fast estimating of heading misalignment is still a challenge due to the existence of gyro errors. An innovative data processing strategy called forward and backward resolution is proposed for INS initial alignment. The Rauch-Tung-Striebel (RTS) smoothing is applied to obtain the smoothed attitude estimates with the filter information provided by the forward data processing. The obtained attitudes are then treated as aiding measurements to implement the forward resolution with the repeated data set, the converged sensor biases are used as constraints, and the iterative processing is conducted to obtain the updated attitudes. Simulation studies have been conducted to validate the proposed algorithm. The results have shown that the alignment accuracy and convergence rate have been improved with the added RTS aided forward and backward resolution; more stable heading estimates can be obtained by calibrating with estimated gyro bias. A real test with a high quality inertial sensor was also carried out to validate the effectiveness of the proposed algorithm.
... Based on the principle of strap-down gyrocompass alignment, Cheng Xianghong from Southeastern University points out that the carrier velocity can affect the gyrocompass alignment and puts forward a calibration method for the inertial sensors of the SINS in the process of alignment on moving base [10]. Yan Gongmin from Northwestern Polytechnical University proposes a calculate method applied to the strap-down gyrocompass alignment on moving base [11]. As gyrocompass alignment needs less calculation amount but still remains reliable, it has a great application prospect for marine SINS alignment. ...
An improved method of gyrocompass alignment for strap-down inertial navigation system (SINS) on moving base assisted with Doppler velocity log (DVL) is proposed in this paper. After analyzing the classical gyrocompass alignment principle on static base, implementation of compass alignment on moving base is given in detail. Furthermore, based on analysis of velocity error, latitude error, and acceleration error on moving base, two improvements are introduced to ensure alignment accuracy and speed: (1) the system parameters are redesigned to decrease the acceleration interference and (2) a data repeated calculation algorithm is used in order to shorten the prolonged alignment time caused by changes in parameters. Simulation and test results indicate that the improved method can realize the alignment on moving base quickly and effectively.
The multi-node multi-baseline Interferometric Synthetic Aperture Radar (InSAR) measurement system is the future development trend in the field of aviation remote sensing. The deformation error between the nodes directly affects the Position and Orientation error compensation of the load at the node, and the baseline length error directly affects the Interferometric phase error compensation. Aiming at the particularity of the wing structure, a structural deformation measurement and baseline measurement method based on strain mode is proposed. The Fiber Bragg Grating (FBG) sensor is used to acquire the strain of each measuring point on the wing in real time, and the deformation of the wing is accurately measured by the modal superposition method, thereby realizing the high-precision measurement of the baseline between the nodes. The measurement result of the dual total station is the benchmark, the validation of the method was carried out on the existing test system in the laboratory. Under static conditions, the experimental test results show that the wing deformation measurement accuracy is better than 1mm, and the baseline measurement accuracy is better than 0.5mm.
This paper presents a new robust adaptive filtering method for transfer alignment by taking into account the systematic errors of observation and kinematic models in the filtering process. The proposed method overcomes the limitation of the traditional Kalman filter, that is, the requirement of precise kinematic and observation models for transfer alignment. It adaptively adjusts and updates the prior information through the equivalent weighting matrix and adaptive factor to resist the disturbances of systematic model errors on system state estimation, thus improving the accuracy of state parameter estimation. Experimental results and comparison analysis demonstrate that the proposed robust adaptive filtering method can effectively improve the accuracy of transfer alignment, and the achieved accuracy is much higher than those of the Kalman and traditional robust adaptive Kalman filters.
