@article{chowdhury_sinha_mahler_guvenc_2023, title={Mobility State Detection of Cellular-Connected UAVs Based on Handover Count Statistics}, volume={4}, ISSN={["2644-1330"]}, url={https://doi.org/10.1109/OJVT.2023.3296138}, DOI={10.1109/OJVT.2023.3296138}, abstractNote={Estimating the speed of aerial user equipment (UE) is critically important to provide reliable mobility management for cellular-connected unmanned aerial vehicles (UAVs) since this can enhance the quality of service. The 3GPP standard uses the number of handovers made by a UE during a predefined time period to estimate the speed and the mobility state efficiently. In this article, we introduce an approximation to the probability mass function of handover count (HOC) as a function of a cellular-connected UAV's height and velocity, HOC measurement time window, and different ground base station (GBS) densities. Afterward, we derive the Cramer-Rao lower bound (CRLB) for the speed estimate of a UAV and also provide a simple biased estimator for its speed based on the GBS density and HOC measurement period. We show that for a low handover parameter, the biased estimator turns into a minimum variance unbiased estimator (MVUE). Using this estimator, we study the problem of detecting the mobility state of a UAV as low, medium, or high mobility as per the 3GPP specifications. Using our proposed MVUE, we also characterize the accuracy improvement in speed estimation and mobility state detection as the GBS density and the HOC measurement window increase.}, journal={IEEE OPEN JOURNAL OF VEHICULAR TECHNOLOGY}, author={Chowdhury, Md Moin Uddin and Sinha, Priyanka and Mahler, Kim and Guvenc, Ismail}, year={2023}, pages={490–504} }
 @article{sinha_guvenc_2022, title={Impact of Antenna Pattern on TOA Based 3D UAV Localization Using a Terrestrial Sensor Network}, volume={71}, ISSN={["1939-9359"]}, url={https://doi.org/10.1109/TVT.2022.3164423}, DOI={10.1109/TVT.2022.3164423}, abstractNote={In this article, we explore the fundamental limits of the 3-dimensional (3D) localization of unmanned aerial vehicles (UAVs) in conjunction with the effects of 3D antenna radiation patterns. Although localization of UAVs has been studied to some extent in the literature, effects of antenna characteristics on 3D localization remains mostly unexplored. To study such effects, we consider a scenario where a fixed number of radio-frequency (RF) sensors equipped with single or multiple dipole antennas are placed at some known locations on the ground, and they derive the time-difference-of-arrival (TDOA) measurements from the time-of-arrival (TOA) data collected for the UAV that is also equipped with a dipole antenna. We then use these measurements to estimate the 3D location of the UAV, and to derive the Cramer-Rao lower bounds (CRLBs) on the localization error for various orientations of the dipole antennas at the transmitter and the receiver. Namely, we consider vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) radiation patterns in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the UAV altitude and identify the respective critical altitudes for each of the VV, VH and HH orientations. Subsequently, we propose a multi-antenna signal acquisition technique that mitigates the accuracy degradation due to the antenna pattern mismatches, and we derive the localization CRLB for the multi-antenna scenario. Our numerical results characterize achievable localization accuracy for various antenna configurations, UAV heights, and propagation conditions for representative UAV scenarios.}, number={7}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sinha, Priyanka and Guvenc, Ismail}, year={2022}, month={Jul}, pages={7703–7718} }
 @article{sinha_krim_guvenc_2022, title={Neural Network Based Tracking of Maneuvering Unmanned Aerial Vehicles}, ISSN={["1058-6393"]}, DOI={10.1109/IEEECONF56349.2022.10052072}, abstractNote={The motion model of an unmanned aerial vehicle (UAV) is a time-varying function that is often unknown to the tracking entity, which makes accurate tracking of highly maneuvering UAVs a challenging problem. Most state-of-the-art tracking techniques employ a fixed set of parametric models that approximate the possible maneuvers along the target trajectory to a reasonable accuracy. However, such predetermined motion models might not be adequate for frequent and aggressive maneuvers performed by small UAVs. To this end, we build a data driven adaptive filtering algorithm that improves the tracking accuracy by using a recurrent neural network (RNN)-based motion model that is trained on realistic simulated data generated from a medium fidelity simulink model of a fixed-wing UAV. We then train another feed-forward neural network in conjunction with the pretrained RNN-based motion model, to adaptively combine the incoming current measurement with the predicted state based on the output of a change detection algorithm that detects any increase/decrease in the uncertainty in the predicted states. Our analysis and results show that the proposed tracking algorithm outperforms the state-of-the-art interactive multiple model (IMM) algorithm for highly maneuvering trajectories.}, journal={2022 56TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS}, author={Sinha, Priyanka and Krim, Hamid and Guvenc, Ismail}, year={2022}, pages={380–386} }
 @article{sinha_guvenc_gursoy_2021, title={Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks}, volume={2}, ISSN={["2644-125X"]}, url={https://doi.org/10.1109/OJCOMS.2021.3109105}, DOI={10.1109/OJCOMS.2021.3109105}, abstractNote={Detection of drones carries critical importance for safely and effectively managing unmanned aerial system traffic in the future. Given the ubiquitous presence of the drones across all kinds of environments in the near future, wide area drone detection and surveillance capability are highly desirable, which require careful planning and design of drone sensing networks. In this paper, we seek to meet this need by using the existing terrestrial radio frequency (RF) networks for passive sensing of drones. To this end we develop an analytical framework that provides the fundamental limits on the network-wide drone detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the drone signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detection performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the drones.}, journal={IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sinha, Priyanka and Guvenc, Ismail and Gursoy, M. Cenk}, year={2021}, pages={2111–2130} }