RF Anomaly Detection using AI in Counter UAS

Extending the range of detection for detecting and disrupting unauthorized drones using a network of distributed drone disruption units. The method involves monitoring drones and their controllers in multiple areas using local drone disruption units. If a drone is detected in a monitored area, nearby drone disruption units are notified. They then collaboratively disrupt the drone's communication link to force it to land. This leverages local resources to extend the effective range of drone detection and disruption beyond the capabilities of individual systems.

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Modular RF systems for locating, identifying, and tracking objects in 3D space using directional antennas. The systems have compact, movable enclosures with integrated cooling and thermal management. The enclosures house modules like RF, power, and processing. Directional antennas are attached to the exterior. The systems leverage machine learning to identify object types from captured RF signals. Based on the type, they generate customized signals and transmit them from the directional antennas. This allows targeted transmissions vs omnidirectional. Multiple systems can share custom signals. The modular design allows swapping antennas for different applications.

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UAVs equipped with neural networks to resist electronic attacks that try to hijack or interfere with their flight. The UAVs have onboard neural networks that analyze real-time sensor data to detect cyber attacks and respond appropriately. The networks analyze images, GPS, and communications to identify attacks like spoofing, jamming, or disorientation. They can then autonomously mitigate the attacks, like continuing the flight path or seeking better connectivity, without being compromised.

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A smart surveillance network for detecting and tracking non-cooperative and cooperative air and watercraft, enabling safe beyond-visual-line-of-sight (BVLOS) operations of unmanned aircraft systems (UAS) and unmanned surface vessels (USV). The system integrates radar, Automatic Dependent Surveillance-Broadcast (ADS-B), Automatic Identification System (AIS), and counter-UAS sensors to provide real-time situational awareness, automatic sensor performance assessment, and risk-based traffic analysis. It enables authorities to certify airspace or waterways for BVLOS operations, monitor compliance with regulations, and detect potential safety threats.

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Passive sensing of unmanned aircrafts (UAs) using forward scatter effect of radio frequency (RF) signals transmitted by satellites and/or aerostats. The system comprises passive RF sensor units that receive and process RF signals to detect and track UAs within a target volume. Multi-sensor analysis units and a central analysis unit process data from multiple sensor units to provide UA situational surveillance, including target detection, tracking, classification, and event prediction.

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A method for detecting micro-drones in urban environments using a combination of active and passive sensors. The method involves recording acoustic signals from the drone's propellers and optical signals from the drone's mechanical vibrations, and then correlating these signals to detect the drone. The active sensors include a millimeter-wave radar operating in the range of resonant absorption of radio waves in the atmosphere, and an active acoustic sensor operating in the range of resonant scattering of sound waves on the drone. The passive sensors include an optical sensor operating in the visible and infrared ranges. The method enables reliable detection of micro-drones in dense urban environments with high levels of interference.

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Detecting flying objects beyond visual line of sight (BVLOS) using computer vision on radar data to enable safe beyond-line-of-sight drone operation. The method involves converting radar buffer images into radial frames, subtracting a background, denoising, tracking objects, and identifying flying objects using CV techniques like contour detection. This allows detecting and avoiding other aircraft in BVLOS airspace by processing radar data like a radar screen would be seen by an operator.

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Unmanned aerial vehicles (UAVs) are becoming more widely used in various industries, raising growing concerns about their safety and reliability. The flight data of UAVs can directly reflect their flight health status; however, the rarity of abnormal flight data and the spatiotemporal characteristics of these data represent a significant challenge for constructing accurate and reliable anomaly detectors. To address this, this study proposes an anomaly detection framework that fully considers the temporal correlations and distribution characteristics of flight data. This framework first combines a one-dimensional convolutional neural network (1DCNN) with an autoencoder (AE) to establish a feature extraction model. This model leverages the feature extraction capabilities of the 1DCNN and the reconstruction capabilities of the AE to thoroughly extract the spatiotemporal features from UAV flight data. Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss,... Read More

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A method and system for identifying aerial vehicles using passive electromagnetic scattering structures attached to rotors. The structures generate unique micro-Doppler signatures during rotation, which are analyzed using machine learning algorithms to identify the vehicle. The system enables reliable identification of small aerial vehicles without active transponders, providing a passive and secure solution for friend-or-foe identification and tracking in shared airspace.

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Integrated system for low-altitude drone response that enables rapid identification and mitigation of unauthorized drones. The system combines radar, camera, and communication capabilities to detect and analyze drone presence, determine threat classification, and initiate response actions. The system integrates with a central server to manage drone traffic, including automatic threat detection, tracking, and response capabilities.

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Advancements in unmanned aircraft systems (UASs) have led to a considerable increase in unlawful operations of these systems over critical infrastructures and public spaces. In this work, a system that is implemented utilizing software-defined radio and signals of opportunity (SOPs) and is based on the fusion of direction of arrival and passive radar sensor measurements is proposed to counter unlawful UAS/drone activities. Specifically, passive radar methodology applied on SOPs (data collection, disturbance cancellation, cross-ambiguity, and constant-false alarm rate detection functions), along with direction of arrival (DOA) measurements obtained via the multiple signal classification algorithm, are combined to detect and track the rogue UAS/target in an area under observation. A prototype implementation of the detection-and-tracking system, with the use of small embedded processing units and the robot operating system is developed and examined in numerous outdoor experiments to thoroughly evaluate its performance.

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Small Uncrewed Aerial Systems (sUAS), commonly referred to as drones, have become ubiquitous in many domains. Examples range from drones taking part in search-and-rescue operations to drones being used for delivering medical supplies or packages. As sUAS commonly exhibit safety-critical behavior, ensuring their safe operation has become a top priority. Thus, continuous and rigorous monitoring of sUAS at runtime is essential. However, sUAS generate vast amounts of data, for example, multi-variate time series which need to be analyzed to detect potential emerging issues. This poses a significant challenge, due to resource constraints imposed on the onboard computation capabilities of sUAS. To alleviate this problem, we introduce ADAM, a novel adaptive monitoring anomaly detection framework for sUAS. ADAM selectively monitors a subset of data streams, which serve as indicators of anomalous behavior. In the event of a raised alert, ADAM adjusts its monitoring strategy, enabling additional detectors and taking further mitigation actions. We evaluated the effectiveness of ADAM through simu... Read More

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Unmanned Aerial Vehicles (UAVs) gather data efficiently for power line inspection. Anomaly detection is essential for power infrastructure dependability and security. It proposes a Cloud-Enabled Isolation Forest (CEIF) method for UAV-based power line inspection. It improves the isolation forest algorithm's efficiency and scalability in cloud computing. It can process huge UAV inspection datasets by dispersing cloud computing. The technique, which effectively isolates anomalies, is applied to the cloud for fast power line inspection and anomaly identification. It describes the CEIF system's cloud service integration and distributed computing algorithm optimization. Real-world UAV-based power line inspection datasets show it can accurately detect abnormalities with low false-positive rates. It is scalable and robust for improving power infrastructure dependability and security. It allows cloud services to deploy real-world settings to implement different inspection scales.

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Unmanned aerial vehicles (UAVs) are predicted to be widely used in both military and civilian sectors in the coming years due to their high mobility, low cost, and enhancement of the line-of-sight (LoS) conditions in non-terrestrial networks.Nevertheless, this raises some security issues if they are manipulated to cause security threats in restricted locations or to breach user privacy.In order to detect malicious UAVs, radio frequency (RF)-based approaches are adopted to detect ambient RF signals, which can be accomplished with inexpensive RF sensors under both LoS and, in particular, non-line-of-sight (NLoS) conditions.In this paper, we propose a passive detection technique based on received signal strength (RSS), and derive analytical expressions on the detection and false alarm probabilities considering realistic airto-ground (A2G) channel conditions.A novel low-complexity suboptimal detector is also proposed and its performance is compared to the optimal detection.Monte Carlo simulations are used to confirm the accuracy of the derived expressions under the aforementioned channel... Read More

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Anomaly detection for flight data is crucial in maintaining the safety and stability of unmanned aerial vehicles (UAVs), making it a topic of significant research and attention. However, existing anomaly detection methods often ignore the random noise of UAV flight data and lack effective parameter selection, resulting in inadequate anomaly detection performance. Furthermore, current methods generally face the problem of insufficient feature extraction capability. In this paper, a spatio-temporal correlation based on one-dimensional convolutional neural network (1D CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) hybrid neural network with residual filtering (STC-1D CBiAM-RF) data-driven multiple regression framework is proposed for anomaly detection and recovery of UAV flight data. First, a correlation analysis method is used for parameter selection to reduce the dependence on expert knowledge. Second, a multiple regression model fusing attention mechanism is designed. It utilizes 1D CNN-BiLSTM as a feature extractor, guided by the attention mechanis... Read More

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The use of Unmanned Aerial Vehicle (UAV) in various industries is increasing, which places higher requirements on the reliability of UAV. One of the ways to ensure the safety of UAV flights is by detecting anomalies in their flight. However, traditional UAV anomaly detection models have some shortcomings. First, they fail to integrate data from multiple sensors across time and frequency domains, hampering the anomaly detection model's ability to accurately assess the UAV's status. Second, they apply the same prediction error loss to all classes, which result in excessive false positives in some key classes. Finally, most of them used unimodal classification models to process data from multiple heterogeneous sensors, which makes it difficult for the models to extract targeted features. This paper proposes a UAV anomaly detection model based on sensor information fusion and hybrid multimodal neural network (IF-HMNN). Firstly, facilitated by the newly devised Multi-source Heterogeneous UAV Sensor Information Alignment algorithm (MHSIA), IF-HMNN can realize information fusion from multip... Read More

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In recent years, a surging number of unmanned aerial vehicles (UAVs) are pervasively utilized in many areas. However, the increasing number of UAVs may cause privacy and security issues such as voyeurism and espionage. It is critical for individuals or organizations to manage their behaviors and proactively prevent the misbehaved invasion of unauthorized UAVs through effective anomaly detection. The UAV anomaly detection framework needs to cope with complex signals in the noisy-prone environments and to function with very limited labeled samples. This paper proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> , a novel framework that is capable of identifying UAV presence, types and operation modes. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BisSiam</small> converts UAVs signals to bispectrum as the input and exploits a siamese network based contrastive learning model to learn the vector encoding. A sampling mechanism is proposed for optimizing the sample size involved... Read More

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A cognitive electronic warfare (EW) system that learns to counter unknown radar threats in real-time without relying on pre-existing threat databases. The system uses machine learning to analyze radar waveforms, identify emitter characteristics, and determine threat intent. It then generates and optimizes electronic countermeasures (ECMs) based on observed threat behavior, continuously assessing their effectiveness and adapting the ECM strategy as needed. The system operates in a closed-loop fashion, enabling real-time countermeasures against agile and unknown radar threats.

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A system and method for detecting and locating unauthorized unmanned aerial systems (UAS) in restricted airspace around wildfires. The system, called a Mobile Emergency Perimeter System (MEPS), uses multilateration and mesh networking to quickly locate the source of radio frequency signals controlling the UAS. MEPS can be rapidly deployed in various terrain conditions and extreme environments to establish a mobile perimeter around wildfires, enabling authorities to identify and halt unauthorized UAS operations.

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Unmanned aerial vehicle (UAV) detection, tracking, and management system that utilizes a combination of radar, video, audio, and camera sensors to monitor airspace. The system employs advanced object detection algorithms to identify and track UAVs while maintaining accurate spatial relationships. It also enables real-time monitoring of UAVs through camera-based tracking, with automatic camera reorientation to maintain object center-of-view. The system maintains a comprehensive database of detected UAVs, including historical trajectories, to enhance object recognition and tracking capabilities.

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