Evidence map›Paper›PMID 33182236›Full record

ArticleSensors (Basel, Switzerland)2020

mGEODAR-A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements.

Anselm Köhler, Lai Bun Lok, Simon Felbermayr, Nial Peters, Paul V Brennan, Jan-Thomas Fischer

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 43% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 2 countries.

Anselm KöhlerDepartment of Natural Hazards, Austrian Research Centre for Forests (BFW), 6020 Innsbruck, Austria.ORCID 0000-0001-7431-0691
Lai Bun LokDepartment of Engineering, Lancaster University, Lancaster LA1 4YW, UK.ORCID 0000-0002-0033-9173
Simon FelbermayrDepartment of Natural Hazards, Austrian Research Centre for Forests (BFW), 6020 Innsbruck, Austria.
Nial PetersDepartment of Electronic and Electrical Engineering, University College London, London WC1E 6BT, UK.ORCID 0000-0001-6817-6262
Paul V BrennanDepartment of Electronic and Electrical Engineering, University College London, London WC1E 6BT, UK.ORCID 0000-0002-3145-3868
Jan-Thomas FischerDepartment of Natural Hazards, Austrian Research Centre for Forests (BFW), 6020 Innsbruck, Austria.ORCID 0000-0001-5179-6457
Austrian Research Centre for Forests · ATUniversity College London · GBLancaster University · GBManagement Center Innsbruck · AT

Funding

NERC grant NE/N009312/1Royal Society University Research Fellowship UF140021Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung P400P2_186687
6 · The paper itself

Abstract

Radar measurements of gravitational mass-movements like snow avalanches have become increasingly important for scientific flow observations, real-time detection and monitoring. Independence of visibility is a main advantage for rapid and reliable detection of those events, and achievable high-resolution imaging proves invaluable for scientific measurements of the complete flow evolution. Existing radar systems are made for either detection with low-resolution or they are large devices and permanently installed at test-sites. We present mGEODAR, a mobile FMCW (frequency modulated continuous wave) radar system for high-resolution measurements and low-resolution gravitational mass-movement detection and monitoring purposes due to a versatile frequency generation scheme. We optimize the performance of different frequency settings with loop cable measurements and show the freespace range sensitivity with data of a car as moving point source. About 15 dB signal-to-noise ratio is achieved for the cable test and about 5 dB or 10 dB for the car in detection and research mode, respectively. By combining continuous recording in the low resolution detection mode with real-time triggering of the high resolution research mode, we expect that mGEODAR enables autonomous measurement campaigns for infrastructure safety and mass-movement research purposes in rapid response to changing weather and snow conditions.

Indexed as

flow dynamicsFMCW Radargeophysical mass-movementsmovement detectionnatural hazards

Identifiers

PMID33182236
PMCPMC7664914
OpenAlexW3106060529

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.