Multi-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation

Multi-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation

Mohamed Bakry El_Mashade

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Multi-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation

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Abstract

In evolving radar systems, detection is regarded as a fundamental stage in their receiving end. Consequently, detection performance enhancement of a CFAR variant represents the basic requirement of these systems, since the CFAR strategy plays a key role in automatic detection process. Most existing CFAR variants need to estimate the background level before constructing the detection threshold. In a multi-target state, the existence of spurious targets could cause inaccurate estimation of background level. The occurrence of this effect will result in severely degrading the performance of the CFAR algorithm. Lots of research in the CFAR design have been achieved. However, the gap in the previous works is that there is no CFAR technique that can operate in all or most environmental varieties. To overcome this challenge, the linear fusion (LF) architecture, which can operate with the most environmental and target situations, has been presented.

References

21 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Mohamed Bakry El_Mashade. 2022. \u201cMulti-Target Detection Capability of Linear Fusion Approach Under Different Swerling Models of Target Fluctuation\u201d. Global Journal of Computer Science and Technology - H: Information & Technology GJCST-H Volume 21 (GJCST Volume 21 Issue H3).

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GJCST Volume 21 Issue H3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-H Classification I.5.1

Submission ReceivedJune 6, 2021
Peer Review Double Blind
Handling Editor
Accepted June 30, 2021
Published July 15, 2021

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v1.2

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January 15, 2022

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