arXiv subjects

M. Humayun Kabir

Publications and source records attributed to M. Humayun Kabir.

3 recordsLinked to original sources

arXiv2026

Beyond GNSS: Leveraging LEO Satellite Signals of Opportunity for Doppler Positioning

Large constellations of low Earth orbit (LEO) satellites provide ubiquitous global connectivity; yet, their rapid motion introduces significant Doppler shift effects. While challenging for communication, these effects can be leveraged for precise user terminal (UT) positioning, particularly in GNSS-denied environments where traditional satellite-based navigation struggles. Unlike GNSS, most LEO satellites lack dedicated navigation signals, motivating signals-of-opportunity (SOPs) approaches that repurpose existing LEO signals and use publicly available two-line element (TLE) files for orbital information, although ephemeris errors can limit accuracy. This article explores the fundamental principles, challenges, and enabling technologies for LEO-based Doppler shift positioning, highlighting its advantages over conventional non-Doppler methods, and discussing future research directions. Furthermore, to facilitate realistic and reproducible performance evaluation beyond stochastic modeling, we propose SionnaNTN-RT, a ray tracing framework for non-terrestrial network (NTN) built upon Sionna, enabling end-to-end simulation of Doppler-based positioning. Simulation results demonstrate that Doppler-based positioning can achieve high positioning accuracy in both urban and open-sky environments, highlighting its promise for next-generation positioning systems.

cs.IT↗

arXiv2025

PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches.

cs.LG↗

arXiv2024

A Double-Difference Doppler Shift-Based Positioning Framework with Ephemeris Error Correction of LEO Satellites

In signals of opportunity (SOPs)-based positioning utilizing low Earth orbit (LEO) satellites, ephemeris data derived from two-line element files can introduce increasing error over time. To handle the erroneous measurement, an additional base receiver with a known position is often used to compensate for the effect of ephemeris error when positioning the user terminal (UT). However, this approach is insufficient for the long baseline (the distance between the base receiver and UT) as it fails to adequately correct Doppler shift measurement errors caused by ephemeris inaccuracies, resulting in degraded positioning performance. Moreover, the lack of clock synchronization between the base receiver and UT exacerbates erroneous Doppler shift measurements. To address these challenges, we put forth a robust double-difference Doppler shift-based positioning framework, coined 3DPose, to handle the clock synchronization issue between the base receiver and UT, and positioning degradation due to the long baseline. The proposed 3DPose framework leverages double-difference Doppler shift measurements to eliminate the clock synchronization issue and incorporates a novel ephemeris error correction algorithm to enhance UT positioning accuracy in case of the long baseline. The algorithm specifically characterizes and corrects the Doppler shift measurement errors arising from erroneous ephemeris data, focusing on satellite position errors in the tangential direction. To validate the effectiveness of the proposed framework, we conduct comparative analyses across three different scenarios, contrasting its performance with the existing differential Doppler positioning method. The results demonstrate that the proposed 3DPose framework achieves an average reduction of 90% in 3-dimensional positioning errors compared to the existing differential Doppler approach.

cs.IT↗