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1. Robust Moving Target Handoff in GPS-Denied Environments

   Tolman, Skyler; Beard, Randy; Morris, T. Devon; Peterson, Cameron K.; Gupta, Riten (June 2021, IEEEASME transactions on mechatronics)

   Unmanned aerial systems (UAS) are effective forsurveillance and monitoring, but struggle with persistent, long-term tracking due to limited flight time. Persistent trackingcan be accomplished using multiple vehicles if one vehiclecan effectively hand off the tracking information to anotherreplacement vehicle. In this paper we propose a solution tothe moving-target handoff problem in the absence of GPS. Theproposed solution uses a nonlinear complimentary filter forself-pose estimation using only an IMU, a particle filter forrelative pose estimation between UAS using a relative rangemeasurement, visual target tracking using a gimballed camerawhen the target is close to the handoff UAS, and track correlationlogic using Procrustes analysis to perform the final target handoffbetween vehicles. We present extensive simulation results thatdemonstrates the effectiveness of our approach and performMonte-Carlo simulations that indicate a 97% successful handoffrate using the proposed methods. 

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2. A Predictive Control Framework for UAS Trajectory Planning Considering 4G/5G Communication Link Quality

   https://doi.org/10.1109/ICNS58246.2023.10124315  

   Zuo, Rui; Wang, Zixi; Caicedo Bastidas, Carlos E.; Cenk Gursoy, M.; Solomon, Adrian; Qiu, Qinru (April 2023, Integrated Communication, Navigation and Surveillance Conference (ICNS))

   A reliable command and control (C2) data link is required for unmanned aircraft systems (UAS) operations in order to monitor the status and support the control of UAS. A practical realization of the C2 communication and mission data links for commercial UAS operations is via LTE/5G networks. While the trajectory of each UAS directly determines the flight distance and mission cost in terms of energy dissipation, it also has a strong correlation to the quality of the communication link provided by a serving base station, where quality is defined as the achieved signal-to-interference-plus-noise ratio (SINR) required to maintain the control link of the UAS. Due to signal interference and the use of RF spectrum resources, the trajectory of a UAS not only determines the communication link quality it will encounter, but also influences the link quality of other UAS in its vicinity. Therefore, effective UAS traffic management must plan the trajectory for a group of UAS taking into account the impact to the interference levels of other base stations and UAS communication links. In this paper, an SINR Aware Predictive Planning (SAPP) framework is presented for trajectory planning of UAS leveraging 4G/5G communication networks in a simulated environment. The goal is to minimize flight distance while ensuring a minimum required link quality for C2 communications between UAS and base stations. The predictive control approach is proposed to address the challenges of the time varying SINR caused by the interference from other UAS’s communication. Experimental results show that the SAPP framework provides more than 3dB improvements on average for UAS communication parameters compared to traditional trajectory planning algorithms while still achieving shortest path trajectories and collision avoidance. 

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3. Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

   https://doi.org/10.3390/app12020576  

   Kim, Joseph; Atkins, Ella (January 2022, Applied Sciences)

   Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM. 

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4. Harnessing the power of topological data analysis to detect change points

   https://doi.org/10.1002/env.2612  

   Islambekov, Umar; Yuvaraj, Monisha; Gel, Yulia R. (December 2019, Environmetrics)

   Abstract We introduce a novel geometry‐oriented methodology, based on the emerging tools of topological data analysis, into the change‐point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data‐generating process. While the applications of topological data analysis to change‐point detection are potentially very broad, in this paper, we primarily focus on integrating topological concepts with the existing nonparametric methods for change‐point detection. In particular, the proposed new geometry‐oriented approach aims to enhance detection accuracy of distributional regime shift locations. Our simulation studies suggest that integration of topological data analysis with some existing algorithms for change‐point detection leads to consistently more accurate detection results. We illustrate our new methodology in application to the two closely related environmental time series data sets—ice phenology of the Lake Baikal and the North Atlantic Oscillation indices, in a research query for a possible association between their estimated regime shift locations. 

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5. Consensus-Enabled Active Learning for Multi-Agent Systems in Ground Classification of Wetlands

   https://doi.org/10.2514/1.I011337  

   Fernando, Chandima; Basha, Elizabeth; Bradley, Justin; Detweiler, Carrick (March 2025, Journal of Aerospace Information Systems)

   Distributed multi-agent unmanned aerial systems (UAS) have the potential to be heavily utilized in environmental monitoring, especially in wetland monitoring. Deep active learning algorithms provide key tools to analyze the sensed images captured during monitoring and interpret them precisely. However, these algorithms demand significant computational resources that limit their use with distributed UAS. In this paper, we propose a novel algorithm for consensus-enabled active learning that drastically reduces the computational demand while increasing the overall model accuracy. Once each of the UAS obtains a labeled subset of images through active learning, we update the weights of the model for three epochs only on the new images to reduce the computational cost, allowing for an increased operational time. The group of UAS communicates the model weights instead of the raw data and then leverages consensus to agree on updated weights. The consensus step mitigates the impact on weights caused by the updates and generalizes the knowledge of each individual UAS to the whole system, which results in increased model accuracy. Our method achieved an average of 11.15% increase in accuracy over 25 acquisition iterations whilst utilizing only 16.8% of the processor time compared to the centralized method of active learning. 

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