ArticleSensors (Basel, Switzerland)2025
A High-Precision Ionospheric Channel Estimation Method Based on Oblique Projection and Double-Space Decomposition.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- High-Precision Time-of-Arrival Estimation in HF Sensor Networks via Multipath Separation and Independent Tracking.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Accurate ionospheric channel estimation is of great significance for acquisition of ionospheric structure, error correction of remote sensing data, high-precision Synthetic Aperture Radar (SAR) imaging, over-the-horizon (OTH) detection, and the establishment of stable communication links. Traditional super-resolution channel estimation algorithms face challenges in terms of multipath correlation and noise interference when estimating ionospheric channel information. Meanwhile, some super-resolution algorithms struggle to meet the requirements of real-time measurement due to their high computational complexity. In this paper, we propose the Cross-correlation Oblique Projection Pursuit (CC-OPMP) algorithm, which constructs an atom selection strategy for anti-interference correlation metric and a dual-space multipath separation mechanism based on a greedy framework to effectively suppress noise and separate neighboring multipath components. Simulations demonstrate that the CC-OPMP algorithm outperforms other algorithms in both channel estimation accuracy and computational efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.