arXiv subjects

Anouar Boumeftah

Publications and source records attributed to Anouar Boumeftah.

4 recordsLinked to original sources

arXiv2026

Space-Based GNSS Radio Frequency Interference Detection Evaluation Through Multi-Satellite Data Integration

Space-based GNSS reflectometry (GNSS-R) can detect terrestrial radio frequency interference (RFI) through elevated noise power in delay-Doppler map forbidden zones. This study evaluates how constellation size affects detection performance using Level 1 delay-Doppler observations from seven CYGNSS spacecraft collected over three months from the NASA PO.DAAC archive. Four metrics are analysed: detection latency, spatial coverage, spatial coherence, and persistence monitoring reliability. Results show that the full seven-satellite constellation reduces median detection latency by a factor of 4.7 compared with a single satellite and increases interception probability for a 5-minute emission from 2\% to 11.5\%. Median footprint revisit time improves from 5.8 hours to under 2.0 hours. Spatial coherence analysis indicates that a single satellite leaves up to 72\% of source structure unresolved. Persistence monitoring confirms interference onset 39 days earlier than single-satellite deployment. The largest gains occur between one and three satellites, establishing three satellites as the minimum effective constellation size.

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arXiv2026

Resilience Through Escalation: A Graph-Based PACE Architecture for Satellite Threat Response

Modern satellite systems face increasing operational risks from jamming, cyberattacks, and electromagnetic disruptions in contested space environments. Traditional redundancy strategies often fall short against such dynamic and multi-vector threats. This paper introduces a resilience-by-design framework grounded in the PACE methodology, which stands for Primary, Alternate, Contingency, and Emergency, originally developed for tactical communications in military operations. It adapts this framework to satellite systems through a layered state-transition model informed by threat scoring frameworks such as CVSS, DREAD, and NASA's risk matrix. We define a dynamic resilience index to quantify system adaptability and implement three PACE variants including static, adaptive, and epsilon-greedy reward-optimized to evaluate resilience under diverse disruption scenarios. Results show that lightweight, decision-aware fallback mechanisms can substantially improve survivability and operational continuity for next-generation space assets.

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arXiv2025

Leveraging Orbital Dynamics with RF Signal Features for Satellite Multi-Orbit Proximity Threat Detection

Proximity-based interference is a growing threat to satellite communications, driven by dense multi-orbit constellations and increasingly agile adversarial maneuvers. We propose a hybrid simulation framework that integrates orbital maneuver modeling with RF signal degradation analysis to detect and classify suspicious proximity operations. Using the open-source Maneuver Detection Data Generation (MaDDG) library from MIT Lincoln Laboratory, we generate labeled datasets combining impulsive maneuver profiles with radio-frequency (RF) impacts across a range of behavioral intents: routine station-keeping, covert shadowing, and overt jamming. Our approach fuses kinematic features such as range, velocity, acceleration, and Time of Closest Approach (TCA), with RF metrics including Received Signal Strength Indicator (RSSI), throughput, and Jammer-to-Signal Ratio (JSR). These features are further enhanced with temporal derivatives and rolling-window statistics to capture subtle or transient interference patterns. A Random Forest classifier trained on this fused feature set achieves 94.67% accuracy and a macro F1 score of 0.9471, outperforming models using only kinematic or RF inputs. The system is particularly effective in detecting covert threats, such as surveillance or intermittent jamming, that evade RF-only methods.

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arXiv2025

Adaptive Detection of On-Orbit Jamming for Securing GEO Satellite Links

This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground station, with the ability to switch between stationary and time-variant jamming modes. We propose a machine learning-based detection approach, employing the random forest algorithm with principal component analysis (PCA) to enhance detection accuracy in the stationary model. At the same time, an adaptive threshold-based technique is implemented for the time-variant model to detect dynamic jamming events effectively. Our methodology emphasizes the need for the use of orbital dynamics in integrating physical constraints from satellite dynamics to improve model robustness and detection accuracy. Simulation results highlight the effectiveness of PCA in enhancing the performance of the stationary model, while the adaptive thresholding method achieves high accuracy in detecting jamming in the time-variant scenario. This approach provides a robust solution for mitigating the evolving threats to satellite communication in GEO environments.

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