@proceedings {963, title = {Bridging Indoor RFID Localization and Long-Range Sensing: Exploring Energy-Efficient Backscatter Positioning}, year = {2025}, month = {03/2025}, publisher = {HamSCI}, address = {Newark, NJ}, abstract = {

The evolution of backscattering communications has significantly advanced RFID-based localization, particularly in scenarios where cost-effectiveness and low power consumption in harsh environments are critical. Originally introduced during World War II with the concept of corner reflectors, backscatter-based localization has since progressed into ubiquitous indoor tracking and Internet of Things (IoT) applications. However, interference, energy harvesting limitations, and communication range constraints limit its full potential. Recent advancements in RFID-based localization have focused on techniques that optimize power allocation, extend range, and mitigate interference. One approach involves integrating sensing and communication (ISAC) systems with RFID, where Zero-Forcing and Convex Optimization minimize interference while enhancing power allocation. However, as tags move closer to user antennas, interference challenges remain. Another key development in backscattering communications has demonstrated long-range RFID localization by decoupling the carrier frequency from the receiver, achieving distances up to 3.4 km at 868 MHz with only 70 μW power consumption. However, this technique introduced unintended interference with nearby communication bands. More recent methods have addressed this issue by employing frequency shift keying (FSK) modulation, significantly extending reader-tag distances. Building upon these insights, we propose an auxiliary approach: integrating energy-harvesting receiving antennas within RFID localization networks, transforming them into self-powered sensor nodes. This approach opens opportunities for radio applications, where distributed low-power RFID beacons could support experimental localization networks. With backscatter localization techniques progressing in extended range, interference mitigation, and energy efficiency, this study explores their potential for passive RF sensing and citizen science applications. By bridging RFID localization and radio science, we investigate their feasibility in long-range sensing scenarios.

}, author = {Gamal Zayed and Ahmed Saeed and Yehea Ismail} } @proceedings {964, title = {Enhancing Lidar-GPS Fusion for Localization: Integrating Ionospheric Modeling with NeQuick G and V2V Implementation}, year = {2025}, month = {03/2025}, publisher = {HamSCI}, address = {Newark, NJ}, abstract = {

Lidar (Light Detection and Ranging) provides high-resolution mapping and odometry for localization but faces limitations when used alone. Conversely, GPS positioning is impaired by ionospheric irregularities{\textemdash}such as Total Electron Content (TEC) variations, scintillation, and gravity waves{\textemdash}which distort satellite signals and reduce accuracy. This work explores a multi-modal fusion approach that integrates Lidar and GPS to optimize localization by compensating for ionospheric-induced errors. In the first phase, we focus on enhancing the GPS component of the Lidar-GPS fusion using the Galileo-based NeQuick G model, a mathematical framework for simulating ionospheric variations. Utilizing NASA{\textquoteright}s Archive of Space Geodesy Data, we analyze the spatio-temporal distribution of Vertical TEC (VTEC). By applying Particle Swarm Optimization (PSO), we identify global VTEC maxima at different altitudes, shedding light on worst-case ionospheric disturbances. For example, our analysis predicts that on January 1, 2027, at 12:00 AM UTC, the highest VTEC values occur at coordinates (-9.8, -88) near Peru, a critical region where GPS accuracy may degrade. This phase aids in forecasting GPS performance fluctuations in high-disturbance zones and informs the optimization of Lidar-GPS fusion for precision applications, including autonomous vehicles and V2V communication. The second phase is an aspirational extension aimed at implementing a Vehicle-to-Vehicle (V2V) communication model using Lidar sensors. This planned work will address challenges such as third-vehicle occlusion and background visible light noise. By integrating Lidar landmark matching, SLAM, and 3D point cloud processing, we intend to enhance localization reliability in dynamic multi-vehicle environments. Ultimately, this study bridges atmospheric science and vehicular localization, demonstrating the synergy between Lidar-based SLAM, ionospheric modeling, and the potential for robust Lidar-driven V2V networking to improve autonomous navigation and road safety.

}, author = {Gamal Zayed and Ahmed Saeed and Yehea Ismail} } @proceedings {935, title = {High-Frequency Signal Strength Simulation at GRAPE Personal Space Weather Stations Using PHaRLAP Ray Tracing}, year = {2025}, month = {03/2025}, publisher = {HamSCI}, address = {Newark, NJ}, abstract = {

High-frequency (HF) radio wave propagation serves as both a vital communication tool and a probe for ionospheric behavior. This work models and analyzes the reception of HF signals from the standard transmitting station WWV (Fort Collins, Colorado) to two well-calibrated Personal Space Weather Stations (PSWS): W2NAF (Scranton, Pennsylvania) and K6KPH (Point Reyes, California). These stations are equipped with Great Radio Amateur Propagation Experiment (GRAPE) receivers{\textemdash}GPS-disciplined systems designed to measure standard time signals with high precision. To simulate HF wave propagation, we use the Provision of High-Frequency Raytracing Laboratory for Propagation Studies (PHaRLAP), a MATLAB toolbox for ionospheric ray tracing. Utilizing the data-based International Reference Ionosphere (IRI) 2020 model, which supplies the ionospheric background conditions, we analyze HF propagation at 5, 10, and 15 MHz by launching a fan of rays at varying elevation angles to determine which rays successfully reach the receivers. In addition, we are extending our simulations using the Python-based SAMI3 ionospheric model. We also compute ionospheric HF absorption and compare the modeled values with the observed signal strengths recorded by the PSWS throughout the day, aiming for the predicted absorption to be within one order of magnitude of the observations. This work enhances our understanding of how ionospheric absorption affects HF signal strength across different times of day and seasons, thereby contributing to space science, space weather research, and amateur radio science.

}, author = {Gamal Zayed and Nathaniel Frissell and Kuldeep Pandey} } @proceedings {881, title = {Optimizing Location Estimation with Novel Numerical Solution using Real-Time Transmitting Beacons WSPRlive, Weak Signal Propagation Reporter Protocol, and Friis Propagation Model}, year = {2024}, month = {03/2024}, publisher = {HamSCI}, address = {Cleveland, OH}, abstract = {

The proliferation of real datasets has become indispensable for implementing various processes in real-time scenarios. Our previous project successfully contributed to expanding data grids aimed at predicting the vertical total electron content (vTEC) for the ionosphere. Leveraging Ham Radio Networks in conjunction with data broadcasted from the International Space Station (ISS), and integrating them into the Galileo-based NeQuickG mathematical model, we developed a web-based application tailored to this purpose. In this study, the focus is on generating new data grids for location estimation within a Radio Frequency (RF) environment, relying solely on transmitter identities and measured received powers. The localization process is divided into two stages. Initially, ideal received powers are computed using the Friis propagation model, resulting in a dataset encompassing over 25,000 locations per received power. To ensure reliability, a hardware implementation of Weak Signal Propagation Reports (WSPR)-based beacons, operational 24/7, is utilized globally. This implementation, facilitated by the IntlWSPR project covering 40 spots, features beacons transmitting at 23 dBm, with maximum gains varying according to antenna types: 7 dBi for Skyloop 80-10, 5 dBi for DX Commander ABV, and 2 dBi for Q-Tek Penetrator antennas. With the ideal dataset established as a reference, location estimation becomes feasible by identifying the best beacon identities per received power for the query receiver. However, numerical solutions for the localization system involve relatively lengthy processing times and exhibit mean square localization errors, motivating the second phase of this work. A novel numerical solution enhancement technique, coupled with a designed radius of convergence, significantly accelerates the convergence of the localization system{\textquoteright}s equations with improved accuracy. Future efforts aim to integrate this enhanced approach into the real-time application, with a focus on estimating the WSPR channel propagation model using additional real data provided by the WSPRnet spot database. Although the WSPRnet spot database offers non-contiguous timed datasets, it serves as a cornerstone for training regression Machine Learning models, facilitating further refinement of the localization process.

}, author = {Gamal Zayed} } @proceedings {690, title = {Evaluation of Global Ionospheric TEC Using Simultaneous Observations from Amateur Radio Networks, International Space Station, and NeQuickG Model for Space Weather Prediction}, year = {2023}, month = {03/2023}, publisher = {HamSCI}, address = {Scranton, PA}, abstract = {

Ionospheric electron density plays a significant role in long-distance communications and sky-wave propagation. Prediction of the accurate state of the ionosphere is necessary to understand the accurate signal perturbations thereby estimating the critical parameters for better signal transmission. The space weather impacts on such trans-ionospheric technological systems are evident. In this work, a web application is developed to represent the global day-to-day electron density variations from the NeQuickG model. Also, the ground-based HAM radio broadcast network hop data with different wavelengths (eg. 10 m and 20 m) and simultaneous top-side electron density with space-based International Space Station (ISS) probe data from floating point measurement units are examined. The electron density variations for the year 2017 are clearly represented. Optimization techniques are necessary to frame a denser spatial grid-based ionospheric electron density map from all the observations. It is essential to estimate the optimal weight function that can distribute the observation influence over empty grid bins with minimum error variance through a probabilistic approach. User-understandable metrics development exclusively for Amateur radio operators and civil aviation sectors is focused. In the near future, the developed web-based application could serve as a better visualization platform for space weather forecasting.

This project, Fellowship of the Ionosphere,\ is a Global Finalist in the 2022 NASA Space Apps Challenge.\ NASA Space Apps 2022 had 31,400+ registered participants from 162 counties and territories, with\ over 3000 submissions from 5327 teams. Global Finalists are ranked as one of the top 35 projects from all submissions.

}, author = {Gamal Zayed and Marcin Lesniowski and Pasumarthi Babu Sree Harsha and Matthew Downs and Daniel Metcalfe and Sila Kardelen Karabulut} }