Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Guo, Qiang; Li, Teng; Wu, Xuecheng; Song, Wenming; Huang, Dayu

doi:10.1631/fitee.2200286

Front Inform Technol Electron Eng

2022

DOI: 10.1631/fitee.2200286

|View full text |Cite

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Qiang Guo

Teng Li

Xuecheng Wu³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dual‐labelled multi‐Bernoulli filter based on specific emitter identification

Guan,

2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

In complex electromagnetic environments, airborne passive bistatic radar encounters the challenge of associating emitters with measurements for multi‐target tracking. The authors propose a solution based on specific emitter identification technology. Firstly, generative adversarial networks (GANs) are utilised to extract and classify emitter signals using radio frequency fingerprint (RFF) features. The classification results are then used to construct a set of emitter labels for pre‐processing the measurement data. Subsequently, the pre‐processed measurement data set is input into the labelled multi‐Bernoulli filter framework, which is extended to a dual‐labelled (target label and emitter label) multi‐Bernoulli filter. This filter jointly predicts and updates the multi‐target posterior density, enabling the estimation of multi‐target trajectories. The effectiveness of the proposed algorithm is validated using two experiments. The results demonstrate that the GAN based on RFF features effectively identifies emitter signals. Moreover, the dual‐labelled multi‐Bernoulli filter, based on specific emitter identification, accurately estimates multi‐target trajectories using measurement data from an airborne passive radar of the multi‐transmit single‐receive type. This approach provides a novel and effective solution to the multi‐target tracking problem in complex electromagnetic environments.

show abstract

Dual‐labelled multi‐Bernoulli filter based on specific emitter identification

Guan,

2024

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

Cited by 1 publication

References 37 publications

Dual‐labelled multi‐Bernoulli filter based on specific emitter identification

Dual‐labelled multi‐Bernoulli filter based on specific emitter identification

Contact Info

Product

Resources

About