High-precision initialization and acceleration of particle filter convergence to improve the accuracy and stability of terrain aided navigation

Wang, Rupeng; Chen, Yunsai; Li, Ye; Xu, Pengfei; Shen, Ping

doi:10.1016/j.isatra.2020.10.004

Cited by 13 publications

(3 citation statements)

References 16 publications

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“…In the above formula, the constant term is sorted into . in fact, the variation of the final optimization function is: (14) The core of solving the optimization problem is to find a state variable to minimize the variation of the above formula, so the most direct way is to take the derivative of the above formula, and then make the derivative of the smallest derivative in the definition domain, and then the optimal solution can be obtained: (15) When decreases to a certain extent, the iteration is stopped.…”

Section: Gps Fusion Based On Pose Graph Optimizationmentioning

confidence: 99%

“…For example, the fusion of visual sensors and IMU can be divided into loose and tight coupling [6]. In loose coupling, the Extended Kalman Filter [8] or the Unscented Kalman Filter (UKF) [14] fuses the pose information obtained from the visual sensors and IMU as separate modules. The information fusion used in literature [16] and [17] is of loose coupling.…”

Section: Introductionmentioning

confidence: 99%

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Position Estimation Method for Small Drones Based on Fusion of Multisource Multimodal Data and Digital Twins

Qu,

Cui,

et al. 2024

Preprint

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In response to the issues of sensor noise and accumulated motion errors that lead to low positioning accuracy and insufficient robustness during large maneuvers or continuous flight of small unmanned aerial vehicles (UAVs) in complex environments, this paper proposes a multi-source and multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors including GPS (Global Positioning System), IMU (Inertial Measurement Unit), and visual sensors to complement the strengths and weaknesses of each hardware component, thereby eliminating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, the method introduces a digital twin UAV data source. By utilizing the Extended Kalman Filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multi-source multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

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Section: Gps Fusion Based On Pose Graph Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Position Estimation Method for Small Drones Based on Fusion of Multisource Multimodal Data and Digital Twins

Qu,

Cui,

et al. 2024

Preprint

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show abstract

“…Wang et al [18] indicated that mismatch easily occurs in the region where its gravity-field feature has no significant change. Wang et al [19] showed that TERCOM in the terrain unmatched region is prone to false peaks and mismatch, due to the influence of measurement errors.…”

Section: Introductionmentioning

confidence: 99%

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

Zhao

Zheng

et al. 2022

Remote Sensing

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This paper mainly studies the improvement of the efficiency and out-of-domain reliability of gravity matching navigation for underwater vehicles. To overcome the traversal low-efficiency problem of the traditional terrain contour matching (TERCOM) algorithm and improve the positioning reliability of its out-of-domain mismatches, a novel soft-margin local semicircular-domain re-searching model (SLSR) is proposed by integrating the soft-margin circular grid matching (SCGM) mechanism and the local semicircular grid re-matching (LSGR) mechanism. SCGM uses three times the inertial navigation cumulative error and adds the unit grid resolution as the soft margin boundary to generate the soft-margin circular domain, which contributes to reducing in-domain matching grid points and enhancing the matching efficiency of algorithms. Then the optimal matching position in this soft-margin circular domain is obtained by using the optimization principle of matching indices. LSGR is triggered when the optimal matching position of SCGM is located near the soft-margin circular-domain boundary. It employs this optimal matching point as the center and the unit inertial navigation error as the radius to recreate the semicircular local re-searching matched grid domain (termed as semicircular domain). Moreover, the optimal matching point in this semicircular domain is obtained by the matching index optimization principle, and then it is compared and updated to obtain the final best matching position of SLSR. The simulation results show that SCGM and LSGR of the proposed SLSR method can effectively improve the matching efficiency and out-of-domain matching reliability of underwater navigation, respectively. Under the same testing conditions for the tracking starting points from three gravity regions, the number of out-of-domain mismatches of SLSR, compared with TERCOM, are lower up to 92.68%, 90.24% and 98.62%, while the average matching accuracies are relatively improved by 88.37%, 85.48% and 83.66%, which verifies the validity and feasibility of the proposed SLSR model on improving the efficiency and out-of-domain reliability of underwater gravity matching navigation.

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Noise filter method for mobile trajectory data

Feng

Zhang

Song

2023

Handbook of Mobility Data Mining

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High-precision initialization and acceleration of particle filter convergence to improve the accuracy and stability of terrain aided navigation

Cited by 13 publications

References 16 publications

Position Estimation Method for Small Drones Based on Fusion of Multisource Multimodal Data and Digital Twins

Position Estimation Method for Small Drones Based on Fusion of Multisource Multimodal Data and Digital Twins

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

Noise filter method for mobile trajectory data

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