Intelligent Fusion Technology, Inc.

United States of America As Represented by the Secretary of the Air Force

Dan Shen

Genshe Chen

Khanh D Pham

Erik Philip Blasch

Xin Tian

Zhonghai Wang

Jingyang Lu

Nichole Sullivan

Gang Wang

Yi Li

Tien M Nguyen

Bin Jia

Xingping Lin

Sixiao Wei

Wenhao Xiong

Kui Liu

Xingyu Xiang

Lun Li

Tao Wang

Biao Chen

Haibin Ling

Carolyn Sheaff

Hua-mei Chen

Jiaoyue Liu

Yiran Xu

Hui Huang

Yajie Bao

Peng Cheng

Roman Ilin

Zhengyang Fan

Tien Nguyen

Hua-Mei Harry Chen

Peter Zulch

Zhihui Shu

Marcello Disasio

Zhi Tian

John Nguyen

Bingwei Liu

A global navigation satellite system (GNSS) is disclosed comprising a transmitter. The transmitter is configured to: receive a plurality of frequencies; determine a minimum frequency from the plurality of frequencies; determine a maximum frequency from the plurality of frequencies; determine a carrier from the minimum frequency and the maximum frequency; determine a set of subcarriers from the carrier and the plurality of frequencies; identify optimal radius of desired phase points to select optimal subcarriers from the set of subcarriers; determine inter-modulation terms with a minimal energy based on the optimal subcarriers; and generate multi-carrier constant envelope waveforms based on the inter-modulation terms.

Method, device and system of multi-carrier constant envelope GNSS waveforms compatible with existing user equipment

A method for detecting GPS spoofing attacks includes providing a trained deep learning (DL) model based on neural networks, feeding GPS signals into the trained DL model, using asymmetric Shapley values (ASVs) to calculate feature contributions, using the ASVs to assign a non-uniform distribution over an ordering of features, obtaining causal structures among the features, applying the ASVs to causal Shapley additive explanation to obtain Shapley attributions, incorporating the Shapley attributions and the causal structures, and detecting GPS spoofing attacks by running the trained DL model and using the causal structures, non-uniform distribution, feature contributions, and Shapley attributions.

Systems and methods to detect GPS spoofing attacks

The present disclosure provides a cross-correlation based method, a system and a storage medium for blind electromagnetic interference Doppler estimation from a single satellite geolocation system. The method includes at a first time, calculating a power spectral density (PSD) of a received signal; smoothing the PSD of the received signal using moving window average, and saving the smoothed PSD of the received signal as PSD0; at a next time, calculating a PSD of another received signal; smoothing the PSD of the another received signal using moving window average, and saving the smoothed PSD of the another received signal as PSD1; performing cross correlation between PSD0 and PSD1 to obtain a cross-correlation result; determining a peak position from the cross-correlation result; and obtaining a Doppler estimation based on a peak position shift between the peak position and a reference position.

System, method, and storage medium for design and use reference emitters to enhance passive single satellite geolocation of EMI sources

A method for recognizing a low-probability-of-interception (LPI) radar signal waveform includes: obtaining, by a radar signal receiver, an LPI radar signal s(t), s(t) varying with time t; extracting, by a radar signal processor, an adaptive feature and a pre-defined analytical feature from the LPI radar signal s(t); combining, by the radar signal processor, the adaptive feature with the pre-defined analytical feature to generate a constructed adaptive feature; and applying, by the radar signal processor, a convolutional neural network (CNN) model to classify the constructed adaptive feature to recognize the LPI radar signal waveform.

Method and system for low-probability-of-intercept radar signal waveform recognition

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

The present disclosure provides a method, a system and a storage medium of PID-based automatic gain control for a satellite transponder system. The method includes receiving a sequence of sample signals; determining two different block sizes where a block size of a first block is greater than a block size of a second block; using the block size of the first block to compute a first signal-amplitude-ratio (SAR)-based gain value and using the block size of the second block to compute a second signal-amplitude-ratio (SAR)-based gain value by the AGC processor through the sequence of sample signals; calculating a to-be-applied gain control value of an m-th transmitted symbol at a n-th time step; and calculating a new AGC gain at the n-th time step according to the to-be-applied gain control value at the n-th time step and a corresponding AGC gain at the (n−1)-th time step.

Method, system and storage medium of proportional-integral-derivative based automatic gain control for satellite transponder system under partial-time partial-band additive white gaussian noise jamming

Embodiments of the present disclosure provide a method, a device, and a storage medium for hybrid automatic gain control in a communication system. The method includes amplifying an input signal to generate an amplified signal which is converted into a plurality of output signals; obtaining a moving average amplitude of a plurality of output signals of a current input signal; and calculating a signal amplitude difference according to the moving average amplitude and a desired output signal amplitude level; calculating a signal amplitude ratio according to a plurality of output signals of two previous output signal blocks and AGC gains corresponding to the plurality of output signals of the two previous output signal blocks; obtaining a step size according to the signal amplitude difference and the signal amplitude ratio; and calculating an AGC gain of the current input signal according to the step size and a corresponding previous AGC gain.

Method, device, and storage medium for hybrid automatic gain control in communication system

A system and method are provided for enhanced multi-way time transfer for time synchronization between at least one slave node and one master node. A slave node sends a first message to the master node to launch a time synchronization between the slave node and the master node. Upon receiving the first message, the master node adds a receiving time on a master clock to the first message to form a second message. The master node sends the second message back to the slave node and the slave node adds a receiving time on the slave clock to the second message to form an updated message. The slave node performs a time adjustment to the slave clock based on the updated message, thereby synchronizing time between the slave node and the master node.

Methods and systems for time synchronization among unmanned aerial systems

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

A system for hybrid modulation and demodulation includes a transmitter and a receiver. The transmitter is configured to receive a hybrid signal of a space-ground link system (SGLS), including a first component and a second component; perform a double sideband (DSB) modulation on the first component using a carrier frequency to obtain a first waveform; perform a single sideband (SSB) modulation on the second component using the carrier frequency to obtain a second waveform; mix the first waveform and the second waveform to generate a hybrid waveform; and transmit the hybrid waveform. The receiver is configured to receive the hybrid waveform; determine the carrier frequency; separate the first waveform and the second waveform; perform a DSB demodulation on the first waveform to obtain a first demodulated signal; and perform an SSB demodulation on the second waveform to obtain a second demodulated signal.

Method and system for hybrid modulation and demodulation

Various embodiments provide a method for free space optical communication performance prediction method. The method includes: in a training stage, collecting a large number of data representing FSOC performance from external data sources and through simulation in five feature categories; dividing the collected data into training datasets and testing datasets to train a prediction model based on a deep neural network (DNN); evaluating a prediction error by a loss function and adjusting weights and biases of hidden layers of the DNN to minimize the prediction error; repeating training the prediction model until the prediction error is smaller than or equal to a pre-set threshold; in an application stage, receiving parameters entered by a user for an application scenario; retrieving and preparing real-time data from the external data sources for the application scenario; and generating near real-time FSOC performance prediction results based on the trained prediction model.

Method and system for free space optical communication performance prediction

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 satellite communication framework includes a satellite system controller; at least one satellite transponder; and a plurality of remote terminals, each including a modem, a router, and a terminal agent. The terminal agent is configured to, based on a current allowable data rate and measurements of a current router queue size and a current router packet arrival rate, use a delayed uplink resource assignment for each modem and an MCV-based flow-control policy to forecast a future router queue size and a future router packet arrival rate and further update the delayed uplink resource request for a time after an uplink allocation delay. The modem is configured to communicate with the router and also with the satellite system controller through the satellite transponder, perform modulation and demodulation, and manage packet loss and delay according to the future router queue size and the future router packet arrival rate.

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Germantown, MD, United States of America

Dan Shen