Abstract: The present disclosure provides a dynamic knowledge graph generation method. The method includes providing a plurality of second-team asset nodes; arranging a plurality of first-team offensive instrument site nodes and a plurality of second-team defensive instrument site nodes; arranging a plurality of first-team offensive instrument nodes and a plurality of second-team defensive instrument nodes; arranging a plurality of first-team deployment nodes and a plurality of second-team deployment nodes; and providing a plurality of engagement nodes. A relationship from a site node to a deployment node is configured as “HOST”; a relationship from an instrument node to a deployment node is configured as “JOIN”; a relationship from a second-team asset node to an engagement node is configured as “TARGET”; a relationship from a second-team deployment node to the engagement node is configured as “DEFEND”; and a relationship from a first-team deployment node to the engagement node is configured as “OFFEND”.

Abstract: A method for detecting false data injection attacks (FDIAs) on a condition-based predictive maintenance (CBPM) system includes: collecting sensor data from sensors monitoring components of a system maintained by the CBPM system to extract features for a cyberattack detection model and gathering historical data of the system to build a cyberattack knowledge base about the system; combining the sensor data and the historical data to train the cyberattack detection model; using a graphical Bayesian network model to capture domain knowledge and condition-symptom relationships between the sensor-monitored components and the sensors; and based on the cyberattack detection model and the Bayesian network model, detecting the FDIAs on the CBPM system.

Abstract: The present disclosure provides a method for model predictive automatic gain control under additive white Gaussian noise jamming. The method includes predicting a plurality of consecutive signal values by an autoregressive integrated moving average model; calculating a signal average value of the plurality of consecutive signal values; calculating a gain control value using the signal average value of the plurality of consecutive signal values; if the gain control value is greater than a maximum control capability of a AGC processor, using the gain control value as a desired gain control value; or if the gain control value is equal to or less than the maximum control capability, using a minimum difference between an estimated amplitude and each of reference amplitudes in the LUT as the desired gain control value; and calculating a new AGC gain for a current time step according to the desired gain control value.

Abstract: The present disclosure provides a system of distributed edge computing for cooperative augmented reality with mobile sensing capability. The system includes a plurality of nodes configured to generate a plurality of data streams; and a plurality of distributed edge servers configured to process one or more tasks using the plurality of data streams. An Apache Storm distributed stream processing platform is installed and properly configured on each distributed edge server; the plurality of distributed edge servers includes one or more service modules installed on each distributed edge server and configured to process the one or more tasks; and the plurality of distributed edge servers includes a master distributed edge server and a plurality of slave distributed edge servers; and a scheduler is installed on the master distributed edge server and configured to distribute the one or more tasks to the plurality of distributed edge servers.

Abstract: The present disclosure provides a deep reinforcement learning (DRL) based dynamic network traffic management system including a LAN router, a plurality of WAN routers, a network switch, and a GNAT controller configured to measure one or more traffic states of a plurality of data flows, obtain an expected reward at the current time point, obtain the one or more traffic states to input to a DRL model to provide an expected reward of each data flow estimated for a next time point, obtain a target reward at the current time point, adjust parameters of the DRL model, predict a plurality of long-term rewards using the trained DRL model, select one of the plurality of long-term rewards, and adjust the bandwidth assigned to each data flow based on the selected long-term reward.

Abstract: The present disclosure provides an ionosphere estimation method applied to a single satellite, an ionosphere estimation system, and a storage medium. The method includes providing locations of the single satellite and an enhanced reference emitter (ERE); using the locations of the single satellite and the ERE to obtain a measured ionospheric delay and transmitting the measured ionospheric delay to the ERE; determining a measured STEC using the measured ionospheric delay; and updating a NeQuick-G model deployed in one or more of the plurality of EREs by implementing a cUKF.

Abstract: The present disclosure provides a deep reinforcement learning (DRL) based dynamic network traffic management system including a LAN router, a plurality of WAN routers, a network switch, and a GNAT controller configured to measure one or more traffic states of a plurality of data flows, obtain an expected reward at the current time point, obtain the one or more traffic states to input to a DRL model to provide an expected reward of each data flow estimated for a next time point, obtain a target reward at the current time point, adjust parameters of the DRL model, predict a plurality of long-term rewards using the trained DRL model, select one of the plurality of long-term rewards, and adjust the bandwidth assigned to each data flow based on the selected long-term reward.

Abstract: A signal acquisition method includes: establishing a sub-integration length of a prime code signal to be a half of a symbol duration of navigation data, where the prime code signal is received by a signal receiver and includes a plurality of sub-integration pairs, and each sub-integration pair includes a first sub-integration and a second sub-integration; for each sub-integration, generating a first local replica and a second local replica based on the prime code signal; and executing FFT based convolution on the prime code signal, the first local replica and the second local replica to yield two convolution results; comparing two convolution maximum peak values and selecting convolution maximum peak values with higher magnitude in the sub-integration pair as first and second convolution peak values; and comparing the first convolution peak value and the second convolution peak value to obtain a corresponding IFFT convolution result to be outputted.

Abstract: A method for point cloud compression of an intelligent cooperative perception (iCOOPER) for autonomous air vehicles (AAVs) includes: receiving a sequence of consecutive point clouds; identifying a key point cloud (K-frame) from the sequence of consecutive point clouds; transforming each of the other consecutive point clouds (P-frames) to have the same coordinate system as the K-frame; converting each of the K-frame and P-frames into a corresponding range image; spatially encoding the range image of the K-frame by fitting planes; and temporally encoding each of the range images of the P-frames using the fitting planes.

Abstract: An intelligent cooperative perception system for autonomous air vehicles (AAVs) comprises at least two AAVs, each of the at least two AAVs being provided with at least one object detection sensor.

Abstract: The present disclosure provide a system, a method, and a storage medium for distributed joint manifold learning (DJML) based heterogeneous sensor data fusion. The system includes a plurality of nodes; and each node includes at least one camera; one or more sensors; at least one memory configured to store program instructions; and at least one processor, when executing the program instructions, configured to obtain heterogeneous sensor data from the one or more sensors to form a joint manifold; determine one or more optimum manifold learning algorithms by evaluating a plurality of manifold learning algorithms based on the joint manifold; compute a contribution of the node based on the one or more optimum manifold learning algorithms; update a contribution table based on the contribution of the node and contributions received from one or more neighboring nodes; and broadcast the updated contribution table to the one or more neighboring nodes.

Abstract: The present disclosure provides a method, a system and a storage medium of resilient human-on-the-loop range-only cooperative positioning of a plurality of unmanned aerial vehicles (UAVs). The method includes computing an initial exploitability using an initial distribution and an initial policy; performing a forward updating of a distribution of a portion of the plurality of UAVs, and performing a backward updating of a Q function of each UAV of the plurality of UAVs; for each time step, calculating a dual variable at an (i+1)-th iteration and calculating a policy at an (i+1)-th iteration; computing a ratio of an exploitability at the (i+1)-th iteration over the initial exploitability; and if the ratio is less than or equal to a pre-defined tolerance value, maintaining a policy at the i-th iteration; and if the ratio is greater than the pre-defined tolerance value, using the policy at the (i+1)-th iteration.

Abstract: The present disclosure provides a through-wall detection system and a through-wall detection method. The through-wall detection system includes a synthetic aperture radar (SAR), configured to scan an area of interest behind a wall to obtain SAR scanning data, where the SAR includes a linear frequency-modulated continuous-wave (LFMCW) time-division multiple access (TDMA) multi-input multi-output (MIMO) SAR; a laser range finder, configured to estimate a distance between the SAR and the wall; and further include a mobile device. A data processor is configured to pre-process SAR scanning data of all Tx-Rx antenna pairs and transfer pre-processed SAR scanning data of all Tx-Rx antenna pairs to the mobile device; and the mobile device is configured to process the pre-processed SAR scanning data of all Tx-Rx antenna pairs to generate a 2-dimensional SAR image and determine presence or absence of a target in the area of interest behind the wall.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for aircraft maintenance scheduling includes using a scheduling environment as a reinforcement learning (RL) environment to simulate an operational concept, train an RL agent, and generate aircraft maintenance decisions and explanations; providing a decomposed reward Deep Q-Network (drDQN) algorithm, wherein the drDQN algorithm includes a first Deep Q-Network (DQN) and a second DQN; using the first DQN to maximize a mission accomplishment objective; using the second DQN to minimize a maintenance cost objective; providing a trained drDQN agent; using the trained drDQN agent to obtain the aircraft maintenance decisions and corresponding mission accomplishment and maintenance cost rewards; using a scheduling module to arrange aircraft maintenance activities; and using an explainable module to get reasons to detail why the decisions are made and present tradeoffs between the decisions and non-selected alternatives.

Abstract: 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.

Abstract: 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.

Abstract: 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.