Intelligent Fusion Technology, Inc.

Jingyang Lu

Genshe Chen

Dan Shen

Erik Philip Blasch

Khanh D Pham

Zhonghai Wang

Lun Li

Xingping Lin

Nichole Sullivan

Xingyu Xiang

Wenhao Xiong

Weifeng Su

Hua-mei Chen

Carolyn Sheaff

Yiran Xu

Marcello Disasio

John Nguyen

Roman Ilin

Peter Zulch

Embodiments of the present disclosure provide a method, a device, and a storage medium for domain adaptation for efficient learning fusion (DAELF). The method includes acquiring data from a plurality of data sources of a plurality of sensors; for each of the plurality of sensors, training an auxiliary classifier generative adversarial network (AC-GAN) by a hardware processor with data from each data source of the plurality of data sources, thereby obtaining a trained feature extraction network and a trained label prediction network for each data source; forming a decision-level fusion network or a feature-level fusion network; and training the decision-level fusion network or the feature-level fusion network with a source-only mode or a generate to adapt (GTA) mode; and applying the trained decision-level fusion network or the trained feature-level fusion network to detect a target of interest.

Method, device, and storage medium for deep learning based domain adaptation with data fusion for aerial image data analysis

A method for rapid discovery of satellite behavior, applied to a pursuit-evasion system including at least one satellite and a plurality of space sensing assets. The method includes performing transfer learning and zero-shot learning to obtain a semantic layer using space data information. The space data information includes simulated space data based on a physical model. The method further includes obtaining measured space-activity data of the satellite from the space sensing assets; performing manifold learning on the measured space-activity data to obtain measured state-related parameters of the satellite; modeling the state uncertainty and the uncertainty propagation of the satellite based on the measured state-related parameters; and performing game reasoning based on a Markov game model to predict satellite behavior and management of the plurality of space sensing assets according to the semantic layer and the modeled state uncertainty and uncertainty propagation.

Method and apparatus for rapid discovery of satellite behavior

The present disclosure provides a high power amplifier (HPA) linearization method, applied to a ground hub which includes a predistorter and a PD controller. The ground hub is arranged in a satellite communication system together with a transmitter and a satellite transponder, and the satellite transponder includes an HPA. The HPA linearization method includes determining an initial correction signal based on a physical model with a plurality of PD parameters to compensate AM-AM and AM-PM characteristics of the HPA; receiving a signal from the satellite transponder; determining a reward function for an action taken by the PD controller; examining an action-value function for actions taken in a preset past period; taking an action to adjust the plurality of PD parameters for the PD to generate an updated correction signal; sending the update correction signal to the transmitter to compensate the AM-AM and AM-PM characteristics of the HPA.

Predistorter, predistorter controller, and high power amplifier linearization method

The present disclosure provides a method for wave propagation prediction based on a 3D ray tracing engine and machine-learning based dominant ray selection. The method includes receiving, integrating, and processing input data. Integrating and processing the input data includes dividing a cone of the original millimeter wave (mmWave) into a plurality of sub cones; determining a contribution weight of rays coming from each sub cone to the received signal strength (RSS) at a receiving end of interest; and determining rays coming from one or more sub cones that have a total contribution weight to the RSS larger than a preset threshold value as dominant rays using a neural network obtained through a machine learning approach. The method further includes performing ray tracing based on the input data and the dominant rays to predict wave propagation.

Method and system for wave propagation prediction

The present disclosure provides a method for joint manifold learning based heterogenous sensor data fusion, comprising: obtaining learning heterogeneous sensor data from a plurality sensors to form a joint manifold, wherein the plurality sensors include different types of sensors that detect different characteristics of targeting objects; performing, using a hardware processor, a plurality of manifold learning algorithms to process the joint manifold to obtain raw manifold learning results, wherein a dimension of the manifold learning results is less than a dimension of the joint manifold; processing the raw manifold learning results to obtain intrinsic parameters of the targeting objects; evaluating the multiple manifold learning algorithms based on the raw manifold learning results and the intrinsic parameters to determine one or more optimum manifold learning algorithms; and applying the one or more optimum manifold learning algorithms to fuse heterogeneous sensor data generated by the plurality sensors.

Methods, systems and media for joint manifold learning based heterogenous sensor data fusion

A multiple-input and multiple-output (MIMO) bolt-on device for a single-input and single-output (SISO) radio, a MIMO channel emulator for testing the MIMO bolt-on device, and a MIMO channel emulation method are provided. The MIMO bolt-on device includes: a plurality of antennas, a multi-channel receiver, a plurality of couplers, a micro-controller, and a switch device. The multi-channel receiver includes a plurality of channels for signal transmission. Each coupler is configured to couple the multi-channel receiver with one of the plurality of antennas. The micro-controller is coupled to the multi-channel receiver to compare signals from the plurality of channels, thereby identifying a channel with a highest signal-to-noise (SNR) among the plurality of channels. The switch device is coupled to the micro-controller and configured to select an antenna corresponding to the channel with the highest SNR among the plurality of antennas for a connection between a selected antenna and the SISO radio.

MIMO bolt-on device, MIMO channel emulator, and MIMO channel emulation method

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Germantown, MD, United States of America

Jingyang Lu