Dr. Mengqing Guo Profile

Dr. Mengqing Guo

at Intelligent Fusion Technology Inc

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Publications (2)

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Proceedings Article | 12 April 2021 Presentation + Paper

Three-dimensional convolutional neural network (3D-CNN) for satellite behavior discovery

Dan Shen, Carolyn Sheaff, Mengqing Guo, Erik Blasch, Khanh Pham, Genshe Chen

Proceedings Volume 11755, 117550C (2021) https://doi.org/10.1117/12.2589044

KEYWORDS: Satellites, Convolutional neural networks, Warfare, Strategic intelligence, Sensors, Performance modeling, Data hiding, 3D modeling

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Space superiority includes space protection and space situational awareness (SSA), which require rapid and accurate space object behavioral motion and operational intent discovery. The presence of clutter, in addition to real-time and hidden information constraints, greatly complicates the space awareness decision-making to control both ground-based and space-based surveillance assets. Space is considered as an important concern in modern frontiers because intelligence information from the space has become extremely vital for strategic decisions, which calls for persistent Space Domain Awareness (SDA). The presence of disagreeable actors in addition to real-time and hidden information constraints greatly complicates the decision-making process in satellite behavior detection as well as operational intent discovery. This paper designs and implements 3D-Convoltional Neural Networks (CNNs) for rapid discovery of evasive satellite behaviors from ground-based sensors, which measure the ranges, azimuth angles, and elevation angles in the Adaptive Markov Inference Game Optimization (AMIGO) tool. The novel 3D CNN extends the generic 2d CNN towards analysis from many perspectives. To generate the 3D CNN model, the training and validation data are simulated based on our game theoretic reasoning engine for elusive space behaviors detection, interactive adversary awareness, and intelligent probing. The performance of the 3D CNN is compared with the 2D CNN models from previous work which is shown for a 10% increase in accuracy.

Proceedings Article | 18 May 2020 Presentation + Paper

Enhanced GANs for satellite behavior discovery

Dan Shen, Carolyn Sheaff, Mengqing Guo, Erik Blasch, Khanh Pham, Genshe Chen

Proceedings Volume 11422, 114220F (2020) https://doi.org/10.1117/12.2560546

KEYWORDS: Gallium nitride, Data modeling, Satellites, Network architectures, Sensors, Space operations, Performance modeling, Binary data, Situational awareness sensors, Solar radiation models

Read Abstract +

Space superiority includes space protection and space situational awareness (SSA), which require rapid and accurate space object behavioral motion and operational intent discovery. The presence of clutter in addition to real-time and hidden information constraints greatly complicates the space awareness decision-making to control both ground-based and spacebased surveillance assets. To increase SSA, generative adversarial networks (GANs) are realizable for rapid discovery of evasive satellite behaviors. Although GANs have shown good results in synthesizing real-world images, GANs “remain remarkably difficult to train” and “approaches to attacking this problem still rely on heuristics that are extremely sensitive to modifications”. This paper describes a modification to a game-theoretic approach to incorporate GANs and train the networks using a general sum game theory. The enhanced GAN model for satellite behavior discovery is called Space unveiled Behavior GAN (SuB-GAN) in this paper. The structure includes training the GANs as a repeated game using a Fictious play concept framework, within which the discriminator (resp. generator) is updated according to the best response to the mixture outputs from a sequence of previously trained. In particular, the discriminator outputs converge to the optimum discriminator function and the mixed output from the sequence of trained generators converges to the data distribution. The simulated training datasets are used to demonstrate the enhanced GANs in the SSA domain. The performance the SuB-GAN is compared with the convolutional neural network (CNN) models showing promising results.

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