An ANN-Based Data Fusion Algorithm for INS/CNS Integrated Navigation System

Liu, Di; Chen, Xiyuan

doi:10.1109/jsen.2022.3159662

Cited by 17 publications

(8 citation statements)

References 31 publications

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“…Fusing all the available information into a blended solution will require synchronization among all contributing sensors [ 29 , 68 , 77 ]. The T&E of sensor fusion engines will require a combination of ground truth data, as well as global and local metrics without ground truth data [ 78 ].…”

Section: Review Of Sensor Requirements For A3rmentioning

confidence: 99%

Review of Sensor Technology to Support Automated Air-to-Air Refueling of a Probe Configured Uncrewed Aircraft

Parry

Hubbard

2023

Sensors

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As technologies advance and applications for uncrewed aircraft increase, the capability to conduct automated air-to-air refueling becomes increasingly important. This paper provides a review of required sensors to enable automated air-to-air refueling for an uncrewed aircraft, as well as a review of published research on the topic. Automated air-to-air refueling of uncrewed aircraft eliminates the need for ground infrastructure for intermediate refueling, as well as the need for on-site personnel. Automated air-to-air refueling potentially supports civilian applications such as weather monitoring, surveillance for wildfires, search and rescue, and emergency response, especially when airfields are not available due to natural disasters. For military applications, to enable the Air Wing of the Future to strike at the ranges required for the mission, both crewed and uncrewed aircraft must be capable of air-to-air refueling. To cover the sensors required to complete automated air-to-air refueling, a brief history of air-to-air refueling is presented, followed by a concept of employment for uncrewed aircraft refueling, and finally, a review of the sensors required to complete the different phases of automated air-to-air refueling. To complete uncrewed aircraft refueling, the uncrewed receiver aircraft must have the sensors required to establish communication, determine relative position, decrease separation to astern position, transition to computer vision, position keep during refueling, and separate from the tanker aircraft upon completion of refueling. This paper provides a review of the twelve sensors that would enable the uncrewed aircraft to complete the seven tasks required for automated air-to-air refueling.

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Section: Review Of Sensor Requirements For A3rmentioning

confidence: 99%

Review of Sensor Technology to Support Automated Air-to-Air Refueling of a Probe Configured Uncrewed Aircraft

Parry

Hubbard

2023

Sensors

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“…[1], [2], [3], [4], [5], [8], [9], [10], [11], [13], [14], [53], [54], [55], [56], [57] Neural System FL Human-language issues are transformed into mathematical formulae.…”

Section: Metaheuristic Bat Algorithmmentioning

confidence: 99%

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Abu

Bukhari²,

Adli³

et al. 2022

Journal of Robotics and Control

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Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot.

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“…In contrast, the state estimation methods adopt the state space expressions to establish the state equations and measurement equations, and estimate the state variables based on the measurement under the significance of the optimal estimation criterion. So far, many advanced state estimation filtering algorithms have been successively used in the field of spacecraft attitude estimation by many scholars, including Multiplicative Extended Kalman Filter (MEKF) [8,9], unscented quaternion estimator [10], iterative extended Kalman filter [11], cubature Kalman filter [12,13], complementary filter [14], particle filter [15] and so on.…”

Section: Introductionmentioning

confidence: 99%

A meticulous covariance adaptive Kalman filter for satellite attitude estimation

Xiao,

Jiang,

Fan

et al. 2024

Meas. Sci. Technol.

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Aiming at the problems of model errors, non-Gaussian noise and measurement anomaly in the spacecraft attitude estimation system, this article proposes an improved adaptive filtering method based on covariance matching, which solves the problems of simultaneous dynamics model error and measurement model error in the attitude estimation system, and at the same time, effectively reduces the effects of non-Gaussian noise and large outlier situations occurring in the vector measurement sensor. Firstly, an adaptive filtering algorithm based on the innovation sequence estimation covariance is investigated under the framework of multiplicative extended Kalman filtering (MEKF), which is used to correct process noise covariance, then the Sage-Husa adaptive Kalman filtering (SHAKF) method is combined to correct the measurement noise covariance, and finally the meticulous covariance adaptive multiplicative extended Kalman filter (MCA-MEKF) is designed. the proposed algorithm uses both innovation and SHAKF methods to correct the two covariance matrices simultaneously. Several attitude estimation simulation scenarios are set up to simulate the proposed algorithm in the presence of model errors, non-Gaussian noise, and large outlier. The simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy and robustness.

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An ANN-Based Data Fusion Algorithm for INS/CNS Integrated Navigation System

Cited by 17 publications

References 31 publications

Review of Sensor Technology to Support Automated Air-to-Air Refueling of a Probe Configured Uncrewed Aircraft

Review of Sensor Technology to Support Automated Air-to-Air Refueling of a Probe Configured Uncrewed Aircraft

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

A meticulous covariance adaptive Kalman filter for satellite attitude estimation

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