Lightweight Single-Antenna Direction-of-Arrival Estimation for Curvilinear Trajectories in Mobile Embedded Systems

Accurate direction-of-arrival (DOA) estimation is valuable for spatially selective communication in noisy industrial environments. This work investigates a lightweight single-antenna framework in which receiver motion forms a virtual aperture. The receiver uses onboard inertial measurement unit (IMU) headings and two-way-ranging (TWR) measurements to a known fixed beacon, avoiding GPS, optical tracking, and high-precision external tracking of the mobile receiver. The curvilinear virtual-array MUSIC formulation is evaluated numerically using phase-coherent narrowband snapshots over arbitrary trajectories. Hardware experiments validate a range- domain TWR-IMU bearing estimator using corrected and averaged ranging observations. In a campaign of 100 consecutive four-revolution sweeps, all trials are retained. Phase-aligned accumulation recovers a 123 mm range-modulation amplitude, in close agreement with the measured 120 mm antenna lever arm. The single-sweep bearing precision is 8.2° absolute world-frame accuracy is limited by systematic BNO055 magnetometer drift in the motorized setup. The embedded bearing-estimation computation consumes 144.9 mJ per estimate, approximately 3 % of the measured cycle energy in the tested configuration. These results establish the feasibility of onboard range-domain bearing estimation and motivate future experimental validation of phase-coherent MUSIC during free-form mobile trajectories.

Scalable Transit Delay Prediction at City Scale: A Systematic Approach with Multi-Resolution Feature Engineering and Deep Learning

Urban bus transit agencies need reliable, network-wide delay predictions to provide accurate arrival information to passengers and support real-time operational control. Accurate predictions help passengers plan their trips, reduce waiting time, and allow operations staff to adjust headways, dispatch extra vehicles, and manage disruptions. Although real-time feeds such as GTFS-Realtime (GTFS-RT) are now widely available, most existing delay prediction systems handle only a few routes, depend on hand-crafted features, and offer little guidance on how to design a scalable, reusable architecture. We present a city-scale prediction pipeline that combines multi-resolution feature engineering, dimensionality reduction, and deep learning. The framework generates 1,683 spatiotemporal features by exploring 23 aggregation combinations over H3 cells, routes, segments, and temporal patterns, and compresses them into 83 components using Adaptive PCA while preserving 95% of the variance. To avoid the "giant cluster" problem that occurs when dense urban areas fall into a single H3 region, we introduce a hybrid H3+topology clustering method that yields 12 balanced route clusters (coefficient of variation 0.608) and enables efficient distributed training. We compare five model architectures on six months of bus operations from the Société de transport de Montréal (STM) network in Montréal. A global LSTM with cluster-aware features achieves the best trade-off between accuracy and efficiency, outperforming transformer models by 18 to 52% while using 275 times fewer parameters. We also report multi-level evaluation at the elementary segment, segment, and trip level with walk-forward validation and latency analysis, showing that the proposed pipeline is suitable for real-time, city-scale deployment and can be reused for other networks with limited adaptation.

Enhanced In-Flight Connectivity for Urban Air Mobility via LEO Satellite Networks

Urban Air Mobility (UAM) is the envisioned future of inter-city aerial transportation. This paper presents a novel, in-flight connectivity link allocation method for UAM, which dynamically switches between terrestrial cellular and Low Earth Orbit (LEO) satellite networks based on real-time conditions. Our approach prefers cellular networks for cost efficiency, switching to LEO satellites under poor cellular conditions to ensure continuous UAM connectivity. By integrating real-time metrics like signal strength, network congestion, and flight trajectory into the selection process, our algorithm effectively balances cost, minimum data rate requirements, and continuity of communication. Numerical results validate minimization of data-loss while ensuring an optimal selection from the set of available above-threshold data rates at every time sample. Furthermore, insights derived from our study emphasize the importance of hybrid connectivity solutions in ensuring seamless, uninterrupted communication for future urban aerial vehicles.