Chuyang Yang

Automatic Dependent Surveillance-Broadcast (ADS-B) receiver streams contain irregular updates and within-trajectory gaps that may reflect forecast uncertainty. We evaluate reception-conditioned residual calibration using established global and Mondrian split conformal methods. The KLAF archive contains 99,736,221 observations from 5161 aircraft over 463 observed dates; repeated experiments use 1,001,738 observations from 276 complete aircraft–days. Training-selected reported-state constant velocity achieves a test mean horizontal distance error (MDE) of 0.848 km and root-mean-square error of 1.385 km on 220,061 common predictor cases. A finite-adjacent-heading calculation labels 99.97% of these cases; stable and maneuvering MDEs are 0.578 and 1.993 km. On 220,040 identical raw forecast cases, causal selective preprocessing increases MDE by 0.0054 km (95% day-cluster interval 0.0045–0.0061). At 90% target coverage, nominal and degraded reception radii are 2.974 and 3.666 km, with a degraded-to-nominal ratio of 1.233 (95% interval 1.143–1.284). At 95%, the ratio is 1.175 (1.108–1.209). Degraded empirical coverage is 89.49% and 93.79%, respectively. Wider degraded radii persist across segmentation, reception-threshold, and motion-stratified analyses. These are empirical out-of-time results for eligible, matched forecasts from one selected terminal benchmark; chronological splitting and cluster resampling do not establish formal conformal validity under the observed dependence.

Heterogeneous sensor-network design is a resource-allocation problem in which planners must select sensing technologies and installation sites under budget, uncertain detection performance, component failures, common-cause outages, and response deadlines. This need is especially evident in low-altitude airport airspace monitoring, where non-cooperative Uncrewed Aircraft Systems (UAS), or drones, can disrupt operations and where sensor siting determines whether detection is timely, reliable, and resilient to sensor outages. The framework developed here evaluates three quantities: nominal coverage, failure-adjusted coverage, and timely detection on the least-observed route. A fast placement method is compared with certified upper bounds to quantify solution quality. Across eight real airport geometries, designing for timely detection increases the timely-detection probability on the least-observed route from .376 to .542, while average coverage falls from .714 to .622. Separating the two changes shows that most of that gain comes from changing the design objective, and only a small part from modeling failures, where shared outages have a stronger effect on design choice than independent sensor availability. A water-contamination case reverses the ranking, showing that the appropriate objective depends on the operational setting. The results compare designs under stated assumptions.

Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in search and rescue (SAR) missions to rapidly locate survivors. However, deploying autonomous swarms in low-altitude airspace shared with crewed rescue aircraft poses significant algorithmic and safety challenges. Following PRISMA 2020 guidelines, this systematic review synthesizes 44 peer-reviewed studies (2022–2026) to evaluate the literature across algorithmic optimization, reality-gap limitations, tactical deconfliction, and validation maturity. The synthesis reveals a consistent trend toward decentralized swarms, driven by Deep Reinforcement Learning in dynamic environments and by bio-inspired metaheuristics for static coverage. Despite these algorithmic advancements, the literature exhibits a severe reality gap: approximately 86% of evaluated models rely exclusively on idealized software simulations, abstracting away critical constraints like communication denial and sensor noise. Furthermore, most models assume uncontested airspace and lack the Manned–Unmanned Teaming (MUM-T) and tactical deconfliction protocols necessary for safe coexistence with rescue helicopters. To achieve true operational readiness within the critical “Golden 72 Hours” of disaster response, the discipline must transition toward hardware-in-the-loop and physical field trials, natively integrating airspace deconfliction into core swarm optimization loops.

Airport airspace safety is increasingly threatened by small, unmanned aircraft systems and wildlife that traditional radar cannot detect. While earlier reviews addressed general counter-UAS techniques, individual sensors, or the detection of single objects such as birds or drones, none has systematically reviewed airport-based, multi-sensor surveillance strategies through a safety-theoretical lens. A systematic review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, synthesized recent research on fixed, ground-based aerial detection capabilities for small aerial hazards, specifically unmanned aircraft systems (sUAS) and avian targets, within operational airport environments. Searches targeted English-language, peer-reviewed articles from 2016 through 2025 in Web of Science and Scopus. Due to methodological heterogeneity across sensor technologies, a narrative synthesis was executed. The review of thirty-six studies, analyzed through Reason’s Swiss Cheese Model and Endsley’s Situational Awareness framework, found that only layered multi-sensor fusion architectures effectively address detection gaps for Low-Slow-Small (LSS) threats. Based on these findings, the review proposes seamless integration with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems through standardized data-exchange interfaces, complemented by theoretically grounded risk-based deployment strategies aligning surveillance technology tiers with operational risk profiles, from basic Remote ID receivers in low-risk rural environments to comprehensive multi-sensor fusion at high-density hubs, major airports, and urban vertiports.

Accurate estimation of aircraft operations, such as takeoffs and landings, is critical for airport planning and resource allocation, yet it remains particularly challenging at non-towered airports, where no dedicated surveillance infrastructure exists. Existing solutions, including video analytics, acoustic sensors, and transponder-based systems, are often costly, incomplete, or unreliable in environments with mixed traffic and inconsistent radio usage, highlighting the need for a scalable, infrastructure-free alternative. To address this gap, this study proposes a novel dual-pipeline machine learning framework that classifies pilot radio communications using both textual and spectral features to infer operational intent. A total of 2489 annotated pilot transmissions collected from a U.S. non-towered airport were processed through automatic speech recognition (ASR) and Mel-spectrogram extraction. We benchmarked multiple traditional classifiers and deep learning models, including ensemble methods, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), across both feature pipelines. Results show that spectral features paired with deep architectures consistently achieved the highest performance, with F1-scores exceeding 91% despite substantial background noise, overlapping transmissions, and speaker variability These findings indicate that operational intent can be inferred reliably from existing communication audio alone, offering a practical, low-cost path toward scalable aircraft operations monitoring and supporting emerging virtual tower and automated air traffic surveillance applications.