Evidence map›Paper›PMID 41157398›Full record

ArticleSensors (Basel, Switzerland)2025

Research on Jamming Recognition Based on Time-Frequency Domain Weighted Fusion and Attention Mechanism.

Yao Zhang, Shuai Wang, Lingyi Wu, Yunchao Yao, Xi Pan

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Yao ZhangSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Shuai WangSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Lingyi WuSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Yunchao YaoSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Xi PanSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Funding

National Natural Science Foundation of China U1730109
6 · The paper itself

Abstract

The jamming recognition of target detection aims to achieve rapid judgment and effective response to jamming by analyzing the target echo signals. This paper addresses the shortcomings of the existing methods in terms of jamming recognition capabilities and practical effectiveness and conducts research on jamming recognition based on time-frequency domain fusion and attention mechanism. First, by analyzing the principles of target detection and jamming effects, a multi-terrain random fluctuation model for ground detection is established. Second, the time-frequency domain weighted fusion method is proposed. Taking multi-period time domain + time-frequency domain as jamming recognition information, combined with the attention mechanism, the jamming recognition model based on time-frequency domain weighted fusion and the attention mechanism (TFWF-AM) is established. Then, the single jamming and compound jamming sample sets are established by superimposing the beating signals of target echo and multi-jamming. Finally, the accuracy of the TFWF-AM jamming recognition model is compared with that of existing method models, and the effectiveness of multi-period time domain + time-frequency domain information is verified. The results show that the TFWF-AM jamming recognition model has the highest accuracy for both single jamming and compound jamming, reaching 99.92% and 99.56%, respectively, which is 10.42% and 52.81% higher than that of the feature fusion model. This research holds huge significance for the perception and decision-making of target detection systems in complex electromagnetic environments.

Indexed as

attention mechanismjamming recognitionmulti-terrain random fluctuation modeltime-frequency domain weighted fusion

Identifiers

PMID41157398
PMCPMC12568031

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.