Adaptive 5G Resource Allocation for Multistatic ISAC-Based UAV Detection and Tracking

Unmanned aerial vehicles (UAVs) enable numerous commercial and public-safety applications, yet they also create security risks near critical infrastructure, transportation hubs, and restricted airspace. While integrated sensing and communications (ISAC) can leverage existing wireless networks for UAV surveillance, practical deployment must address competition between sensing and communication demands, as well as the challenges associated with tracking highly maneuverable UAVs with low radar cross section (RCS). This paper investigates adaptive multistatic ISAC for load-aware UAV detection and tracking in 5G wireless networks. A shared-resource framework is developed to quantify how sensing waveform length, sensing transmission rate, and beam allocation affect communication throughput in a 5G new radio (NR) system. Detection performance is analyzed using Zadoff-Chu (ZC) sensing waveforms, while tracking continuity is evaluated through an M-of-N detection model. To improve robustness under congestion, software-defined sensor (SDS) nodes exploit external signals of opportunity (SoO) to provide supplemental passive sensing opportunities when network resources become limited. Results show that adaptive sensing policies outperform fixed sensing reservations by preserving throughput under dynamic load while maintaining useful sensing capability. Under heavy congestion, SDS assistance substantially reduces tracking outage in the simulated scenarios. Cramer-Rao lower bound (CRLB) analysis demonstrates that multistatic sensing geometries improve localization accuracy and provide more uniform spatial coverage than monostatic sensing alone. These results highlight coordinated adaptive sensing and distributed multistatic support as a practical path toward resilient UAV surveillance in future wireless networks.

Fusion of Cellular ISAC and Passive RF Sensing for UAV Detection and Tracking

The rapid growth of unmanned aerial vehicles (UAVs) in civilian and critical-infrastructure airspace has created a need for reliable detection and tracking systems that operate under diverse environmental and sensing conditions. This paper presents a UAV detection and tracking system that fuses measurements from a network of passive Keysight N6841A RF sensors and a Ku-band Fortem TrueView R20 radar operating in the FR3 spectrum (16.3 GHz) as an ISAC proxy. Real-world experiments at the NSF AERPAW testbed demonstrate that radar and RF sensing provide complementary strengths under varying geometric, range, and line-of-sight conditions. A Kalman filter using a constant-velocity motion model integrates the asynchronous 2D RF and 3D radar observations, suppressing large standalone errors, improving accuracy over individual modalities, and increasing tracking coverage without degrading performance. These results demonstrate the effectiveness of multi-modal, ISAC-oriented sensing for robust UAV tracking in outdoor environments.

Collection: Datasets from AFAR Challenge

This paper presents a comprehensive real-world and Digital Twin (DT) dataset collected as part of the Find A Rover (AFAR) Challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) testbed and hosted at the Lake Wheeler Field in Raleigh, North Carolina. The AFAR Challenge was a competition involving five finalist university teams, focused on promoting innovation in UAV-assisted radio frequency (RF) source localization. Participating teams were tasked with designing UAV flight trajectories and localization algorithms to detect the position of a hidden unmanned ground vehicle (UGV), also referred to as a rover, emitting wireless probe signals generated by GNU Radio. The competition was structured to evaluate solutions in a DT environment first, followed by deployment and testing in AERPAW's outdoor wireless testbed. For each team, the UGV was placed at three different positions, resulting in a total of 30 datasets, 15 collected in a DT simulation environment and 15 in a physical outdoor testbed. Each dataset contains time-synchronized measurements of received signal strength (RSS), received signal quality (RSQ), GPS coordinates, UAV velocity, and UAV orientation (roll, pitch, and yaw). Data is organized into structured folders by team, environment (DT and real-world), and UGV location. The dataset supports research in UAV-assisted RF source localization, air-to-ground (A2G) wireless propagation modeling, trajectory optimization, signal prediction, autonomous navigation, and DT validation. With approximately 300k time-synchronized samples collected from real-world experiments, the dataset provides a substantial foundation for training and evaluating deep learning (DL) models. Overall, the AFAR dataset serves as a valuable resource for advancing robust, real-world solutions in UAV-enabled wireless communications and sensing systems.

Impact of Altitude, Bandwidth, and NLOS Bias on TDOA-Based 3D UAV Localization: Experimental Results and CRLB Analysis

This paper investigates unmanned aerial vehicle (UAV) localization using time difference of arrival (TDOA) measurements under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A 3D TDOA Cram\'er-Rao lower bound (CRLB) model is developed accounting for varying altitudes and signal bandwidths. The model is compared to five real-world UAV flight experiments conducted at different altitudes (40 m, 70 m, 100 m) and bandwidths (1.25 MHz, 2.5 MHz, 5 MHz) using Keysight N6841A radio frequency (RF) sensors of the NSF AERPAW platform. Results show that altitude, bandwidth, and NLOS obstructions significantly impact localization accuracy. Higher bandwidths enhance signal time resolution, while increased altitudes mitigate multipath and NLOS biases, both contributing to improved performance. However, hovering close to RF sensors degrades accuracy due to antenna pattern misalignment and geometric dilution of precision. These findings emphasize the inadequacy of traditional LOS-based models in NLOS environments and highlight the importance of adaptive approaches for accurate localization in challenging scenarios.