ArticlePloS one2026
An extended target tracking algorithm based on trajectory feedback constrained spatial information.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately estimating the state of extended targets is a major challenge because measurement numbers and distributions change significantly, especially when tracking targets are close or overlapping. To solve the track fragmentation problem caused by spatial measurement ambiguity, a novel Trajectory Situation Feedback-based Gaussian Mixture Model Expectation Maximization (TSF-GMM-EM) method is proposed within the framework of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. The proposed method feeds historical trajectory information back to guide current measurement partitioning and motion estimation while providing prior-guided initialization for the EM process, thereby improving partitioning reliability during target intersections and accelerating clustering convergence. Experimental results show that the TSF-GMM-EM method limits the peak OSPA error to approximately 12 m during target intersections. It also maintains a measurement partitioning accuracy above 0.946 in overlapping target scenarios.
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.