Abstract Low-cost Global Navigation Satellite System (GNSS) receivers and antennas have emerged as promising alternatives to geodetic-grade equipment for precise positioning and atmospheric monitoring applications. Integrating multiple GNSS constellations, advanced positioning techniques such as Precise Point Positioning (PPP) and Real-Time Kinematic (RTK), and improved antenna designs have significantly enhanced consumer-grade GNSS hardware capabilities for Position, Navigation, and Timing services. This paper surveys PWV estimation using low-cost GNSS receivers, including smartphones (Huawei P40, Huawei Mate 20X), consumer chipsets (u-blox ZED-F9P, u-blox M8T, Piksi Multi, Unicorecomm Nebulas II), and low-cost antennas. Multi-constellation configurations (GPS, GLONASS, Galileo, BeiDou, QZSS) consistently improved accuracy and reduced convergence times. PPP achieved sub-meter to centimeter-level accuracy; RTK demonstrated 2–5 cm horizontal accuracy. Tropospheric ZTD estimates agreed with geodetic equipment to 3–8 mm RMS, and PWV retrievals agreed with radiosonde and reanalysis products to within 1–3 mm. Low-cost GNSS has matured to near-geodetic performance for many applications. Antenna quality remains the primary accuracy differentiator multipath mitigation, ambiguity resolution for noisy receivers, this is a challenging task for improving positional accuracy for GNSS RTK users.
Abstract Global Navigation Satellite Systems (GNSS) constitute a critical positioning layer for intelligent transportation systems, including road mobility, aviation navigation, maritime operations, and emerging autonomous transport applications. The growing exposure of GNSS-enabled transport services to spoofing, jamming, and meaconing attacks creates a pressing need for deployable integrity-monitoring approaches capable of identifying interference without relying on specialized radio-frequency hardware or raw IQ signal access. This paper proposes a leakage-safe receiver-derived framework for GNSS interference recognition based exclusively on navigation-level and receiver-level observables available from standard commercial GNSS devices. The methodology integrates positioning trajectories, radial drift descriptors, local instability metrics, horizontal accuracy estimates, pseudorange-based statistics, Doppler-related variables, and integrity-oriented risk indicators extracted from NAV-PVT, RINEX, and MON-RF measurements. Using the publicly available JammerTest 2024 dataset, a comprehensive preprocessing and feature-engineering pipeline was implemented to construct window-level representations of interference-induced positioning degradation. To avoid overly optimistic performance estimates caused by temporal and scenario overlaps, model evaluation was performed using repeated scenario-group holdout splitting, ensuring strict separation between training and testing scenarios. Window-level classification was subsequently carried out using an ExtraTrees ensemble classifier on 2,939 integrity windows derived from 44,639 positioning samples across 24 spoofing, jamming, and meaconing scenarios. Under this leakage-safe evaluation protocol, the proposed framework achieved a mean classification accuracy of 62.86 % ± 9.58 % and a mean macro-F1 score of 0.5490 ± 0.0980 across repeated scenario-group splits. Feature-importance analysis identified radial drift descriptors, Doppler-related observables, and pseudorange-based statistics among the most discriminative receiver-derived variables. Although classification performance remained lower for meaconing than for spoofing and jamming, the results demonstrate that standard receiver outputs retain operationally meaningful information for interference-aware positioning monitoring even without direct signal-level access. The proposed framework provides a reproducible baseline for resilient GNSS integrity monitoring and practical interference recognition in intelligent transportation systems operating under degraded navigation conditions.
Abstract This study proposes a comprehensive framework for gravity anomaly modeling from multi-mission satellite altimetry, introducing a novel preprocessing strategy based on the Neighbor Gradient Anomaly Detection (NGAD) method. The approach integrates datasets from Jason-1 GM, Jason-2 GM, and Cryosat-2, combining systematic preprocessing, computation of vertical deflections, and gravity anomaly derivation using the inverse Vening Meinesz method through one-dimensional Fast Fourier Transform (1D FFT). Unlike conventional statistical approaches, the NGAD-based method effectively suppresses noise while preserving short-wavelength geophysical signals essential for high-resolution gravity field reconstruction. Validation against independent shipborne gravity data demonstrates strong agreement in spatial patterns, with correlation values comparable to state-of-the-art global models (DTU21, SIO V33). Following track segmentation, NGAD-based outlier removal, Gaussian filtering, and exclusion of low SNR (<10 dB) data, the results show consistent RMSE values across missions, with differences of 1.98–2.36 mGal relative to EGM2008. These differences are linked to enhanced sensitivity to short-wavelength variability rather than reduced model performance. Spectral analysis confirms reliable signal recovery within the 10–20 km wavelength band, while shorter wavelengths remain partially unresolved. The model achieves an average accuracy of 4.77 mGal in deep-water regions (>2,000 m), consistent with previous studies, while accuracy decreases to ∼14 mGal in shallow regions, highlighting bathymetric influence. The proposed framework provides a robust foundation for gravity modeling in the Banda Sea, with significant potential for geophysical exploration in tectonically complex archipelagic regions. No comprehensive gravity anomaly studies have previously been conducted in the Banda Sea, underscoring the novelty and contribution of this work.
Abstract This systematic quantitative literature review analyzes the performance, cost, and environmental sensitivity of low-cost GNSS modules from 2015 to July 2025. Using a PRISMA-guided search of Scopus and Google Scholar, 37 studies were identified, focusing on quantitative metrics such as RMSE and SD. The findings reveal that multi-frequency (MF) modules consistently provide centimetre-level accuracy in both open-sky and urban/kinematic conditions, while dual-frequency (DF) modules show improvement over single-frequency (SF) modules but remain sensitive to urban multipath. SF modules exhibit high accuracy variability depending on environmental conditions. A trade-off exists between cost and accuracy, with DF and MF modules offering higher precision but at a higher cost, suitable for applications like deformation monitoring. The meta-analysis indicates that MF modules outperform DF and SF modules in stability and accuracy across various environments. This review maps module distribution, identifies methodological gaps, and offers practical guidance for selecting GNSS modules based on application and environmental factors, providing valuable insights for cost-effective geospatial monitoring and surveying.
