An improved integrated navigation method with enhanced robustness based on factor graph

Wei, Xiaokai; Li, Jie; Zhang, Debiao; Feng, Kaiqiang

doi:10.1016/j.ymssp.2020.107565

Cited by 23 publications

(17 citation statements)

References 23 publications

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“…In principle, the form of system state equation represents an IMU factor f IMU . The system transfer matrix and covariance can be obtained based on a nonlinear equation about navigation state of position, velocity and attitude [24]. The state x i+1 is calculated from the state and the measurement at time i.…”

Section: Imu Factor;mentioning

confidence: 99%

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

et al. 2022

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Because of the resilient frame structure, the factor graph is often used in navigation systems to solve the sensor asynchrony problem and realize plug-and-play effectively in the navigation information fusion method. To improve the fault detection performance of resilient integrated navigation systems under complex interference environments, a fault detection method in factor graph navigation framework based on INS measurements and GNSS pseudo-range measurements is proposed in this paper. The proposed method can effectively locate the fault satellite pseudo-range information based on the Chi-square fault detection method. Due to the plug-and-play characteristic of the factor graph framework, this method can quickly isolate faults to improve navigation accuracy. Finally, the effect of the method is verified by simulation and experiment. Compared with the Chi-square fault detection method, positioning accuracy is improved by more than 40%.

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Section: Imu Factor;mentioning

confidence: 99%

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

et al. 2022

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“…The factor graph [8][9][10] algorithm is an estimation algorithm based on Bayesian networks, which has shown good performance in many fields. For example, in the navigation problem, there are Wen et al in literature [11], Wei et al in literature [12], Gao et al in literature [13], Liu et al in literature [14], and Xu et al in literature [15], all of which use the factor graph algorithm instead of the Kalman filter algorithm. The factor graph algorithm offers superior performance while also ensuring good robustness.…”

Section: Introductionmentioning

confidence: 99%

A Moving Target Online Location Algorithm Based on Factor Graph

Wang

Zhang

et al. 2023

International Journal of Aerospace Engineering

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The superior performance of factor graphs compared to Kalman filtering in various fields and the use of factor graph algorithms instead of Kalman filtering algorithms in moving target localization tasks can reduce target localization error by more than 50%. However, the global factor graph algorithm may cause computational delays due to excessive computational effort. A moving target localization algorithm based on a combination of global and incremental optimization with improved factor graphs is proposed to improve localization accuracy and ensure that the computation time can be adapted to the requirements of online location. A reference point is introduced into the incremental calculation process, and it is first determined whether global or incremental calculation is used for this calculation by comparing the distance between the incremental localization results of the calculated reference point. The position of the UAV itself is then corrected by determining the position of the reference point, and this is used to finally locate the target. Simulation results show that the algorithm has good real-time performance compared to the time-consuming global algorithm. The online localization error of moving targets can be reduced by 17% compared to the incremental calculation results of the classical factor graph algorithm.

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“…Han [14] proposed a sensor selection and optimization approach based on factor graph that meets the demand of environment adapting in multi-sensor fusion. To improve the navigation performance and robustness of integrated navigation algorithm based on factor graph under the condition that the performance of each sensor changes and the output is abnormal in the actual complex navigation environment, an improved factor graph method based on enhanced robustness is proposed [15]. The navigation and positioning methods via factor graph are considered and classified.…”

Section: Introductionmentioning

confidence: 99%

Personnel Localization Method in Transformer Substation Based on Factor Graph

Yang,

Zhou,

et al. 2023

JCC

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A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.

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An improved integrated navigation method with enhanced robustness based on factor graph

Cited by 23 publications

References 23 publications

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement

A Moving Target Online Location Algorithm Based on Factor Graph

Personnel Localization Method in Transformer Substation Based on Factor Graph

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