An integrated INS/GNSS system with an attention-based hierarchical LSTM during GNSS outage

Taghizadeh, Sina; Safabakhsh, Reza

doi:10.1007/s10291-023-01412-w

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…, ba k+1 , bg k+1 ] T (8) According to Figure 2, the factor node of IMU can be constructed as a binary factor, and the residual function r imu (Z…”

Section: Imu Factor and Bias Factor Modelingmentioning

confidence: 99%

“…Affected by occlusion from complex environments, such as high-rise buildings and tunnels, GNSS may become completely ineffective. Therefore, the urban environment has a significant impact on the navigation accuracy of GNSS [8]. Visual navigation systems have attracted significant attention in autonomous systems, and LiDAR is widely used due to its high precision and high-frequency data acquisition capability.…”

Section: Introductionmentioning

confidence: 99%

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Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Wang,

Zeng,

Shao

et al. 2024

Remote Sensing

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Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

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“…, ba k+1 , bg k+1 ] T (8) According to Figure 2, the factor node of IMU can be constructed as a binary factor, and the residual function r imu (Z…”

Section: Imu Factor and Bias Factor Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Wang,

Zeng,

Shao

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The modeling and mitigation of the multipath pose significant challenges due to its complex nonlinear and time-varying nature. In recent years, deep learning has emerged as a powerful technique for addressing non-linear problems and has been successfully employed in various domains, such as ionosphere forecasting [28,29], troposphere tomography [30], satellite orbit broadcast [31], satellite clock prediction [32], self-driving [33] and integrated navigation [34]. Deep learning algorithms such as neural networks are data-driven models that use large and extensive datasets to obtain correlations without relying on complex physically based models [35].…”

Section: Introductionmentioning

confidence: 99%

Characterization of BDS Multipath Effect Based on AT-Conv-LSTM Network

Sun,

Tang,

Zhou

et al. 2023

Remote Sensing

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Multipath effects are the most challenging error sources for the Global Navigation Satellite System receiver, affecting observation quality and positioning accuracy. Due to the non-linear and time-varying nature, multipath error is difficult to process. Previous studies used a homogeneous indicator to characterize multipath effects and only revealed the temporal or spatial correlations of the multipath, resulting in limited correction performance. In this study, we consider the code multipath to be influenced not only by the elevation and azimuth angle of certain stations to satellites but also to be related to satellite characteristics such as nadir angle. Hence, azimuth angle, elevation angle, nadir angle and carrier-to-noise power density ratio are taken as multiple indicators to characterize the multipath significantly. Then, we propose an Attention-based Convolutional Long Short-Term Memory (AT-Conv-LSTM) that fully exploits the spatiotemporal correlations of multipath derived from multiple indicators. The main processing procedures using AT-Conv-LSTM are given. Finally, the AT-Conv-LSTM is applied to a station for 16 consecutive days to verify the multipath mitigation effectiveness. Compared with sidereal filtering, multipath hemispherical map (MHM) and trend-surface analysis-based MHM, the experimental results show that using AT-Conv-LSTM can decrease the root mean square error and mean absolute error values of the multipath error more than 60% and 13%, respectively. The proposed method can correct the code multipath to centimeter level, which is one order of magnitude lower than the uncorrected code multipath. Therefore, the proposed AT-Conv-LSTM network could be used as a powerful alternative tool to realize multipath reduction and will be of wide practical value in the fields of standard and high-precision positioning services.

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A hybrid optimization method based on DBO-tuning BiGRU assisted AKF for seamless INS/GPS navigation

Wei,

Lang,

Wang

et al. 2024

GPS Solut

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An integrated INS/GNSS system with an attention-based hierarchical LSTM during GNSS outage

Cited by 11 publications

References 30 publications

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Characterization of BDS Multipath Effect Based on AT-Conv-LSTM Network

A hybrid optimization method based on DBO-tuning BiGRU assisted AKF for seamless INS/GPS navigation

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