Mr. Lei Guo Profile

Lei Guo

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Proceedings Article | 29 August 2024 Paper

Multiscale tracking for maneuver target using single sensor

Zhiming Zheng, Chun Cheng, Lei Guo

Proceedings Volume 13249, 132490R (2024) https://doi.org/10.1117/12.3043302

KEYWORDS: Signal filtering, Detection and tracking algorithms, Data modeling, Wavelets, Sensors, Discrete wavelet transforms, Signal processing

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The single-scale filtering method only takes advantage of the fact that it fails to make full use of the characteristics of the observed data at different scales, especially when the system fluctuates drastically, the filtering effect decreases seriously. We propose a novel algorithm of multi-scale fusion and estimation for single sensor target tracking in this paper. Utilizing discrete wavelet transform, we reformulate the state equation and observation equation of Kalman filter in a multi-scale form to found a novel multi-scale Kalman filtering model. By making full use of the signal feature on the diffident scales, the algorithm is more effective for tracking maneuver target, especially in a low signal to noise ratio scenario. A set of Monte Carlo simulations are performed, and the results show the efficiency of the algorithm in this paper.

Proceedings Article | 5 July 2024 Paper

Onboard remote high-definition collaborative troubleshooting and flight decision-making assistance device

Cheng Shao, Jinze Song, Lei Guo

Proceedings Volume 13184, 131840J (2024) https://doi.org/10.1117/12.3033189

KEYWORDS: Data modeling, Decision making, Video, Action recognition, Model based design, Glasses, Image acquisition, Clouds, Modeling, Design

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In the process of aircraft design, various experiments are required to validate design parameters under real flight conditions. Such experiments are costly, and currently there are limited solutions for effectively dealing with scenarios such as occasional faults during these experiments. This paper proposes an on-board remote high-definition collaborative troubleshooting and flight decision-making assistance device, aiming to achieve efficient test scenario replication and decision support through remote collaboration between ground personnel and flight crew. The device utilizes several image capture devices in the cockpit and employs algorithms such as action recognition and image recognition. It utilizes highspeed satellite communication technology to structure flight data, including pilot operations, faults, and aircraft status, into a timeline script. Ground personnel can use this output script to provide corresponding solutions. Flight crew members receive real-time guidance and decision support through smart glasses. It is evaluated that this approach may save a significant amount of travel expenses and time in ground tests, flight tests, field fault diagnosis, and crew training scenarios. The innovation of the proposed device lies in the application of cloud servers for timeline script generation and the use of remote retrieval by monitoring personnel for key operation fault diagnosis. Furthermore, it incorporates intelligent recognition technology for flight guidance, enabling efficient troubleshooting and reducing the number of flight tests. Therefore, research and development costs could be reduced, and engineering change progress could be accelerated.

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