Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network.

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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

0.4field-weighted citation impact, top 42% 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

3 authors at 1 institution in 1 country.

Artjom LindIntelligent Transportation Systems Lab, Institute of Computer Science, University of Tartu, Narva Mnt 18, 51009 Tartu, Estonia.ORCID 0000-0002-8498-3547

Shan WuIntelligent Transportation Systems Lab, Institute of Computer Science, University of Tartu, Narva Mnt 18, 51009 Tartu, Estonia.ORCID 0000-0002-8062-0853

Amnir HadachiIntelligent Transportation Systems Lab, Institute of Computer Science, University of Tartu, Narva Mnt 18, 51009 Tartu, Estonia.ORCID 0000-0001-9257-3858

University of Tartu · EE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter's inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells' coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.

Indexed as

cellular networkGaussian mixture modelgeolocationKalman filtersynthetic data generation

Identifiers

PMID37050661

DOI10.3390/s23073603

PMCPMC10098955

OpenAlexW4361267412

What OpenQuestion holds

Textmetadata

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.