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

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Mohamed Bakry El_Mashade
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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  3. M El Mashade (1999). Partially correlated sweeps detection analysis of mean-level detector with and without censoring in non ideal background conditions.
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  5. M El Mashade (2006). Analysis of Cell-Averaging Based Detectors for χ 2 Fluctuating Targets in Multitarget Environments.
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  7. Santos Lopez-Estrada,René Cumplido (2005). Fusion center with neural network for target detection in background clutter.
  8. Toufik Laroussi,Mourad Barkat,Nassim Benadjina (2007). A Performance Comparison of Two Time Diversity Systems using TM-CFAR Detection for Partially Correlated Chi-Square Targets in Nonuniform Clutter and Multiple Target Situations.
  9. Mohamed El Mashade (2011). Analytical performance evaluation of adaptive detection of fluctuating radar targets.
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  11. Maynul Md,Mohammed Islam,Hossam-E-Haider (2018). Detection Capability and CFAR Loss Under Fluctuating Targets of Different Swerling Model for Various Gamma Parameters in radar.
  12. M El Mashade (2020). Performance superiority of CA_TM model over N-P algorithm in detecting χ<SUP align="right">2</SUP> fluctuating targets with four-degrees of freedom.
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  14. Mohamed El Mashade (2008). PERFORMANCE ANALYSIS OF OS STRUCTURE OF CFAR DETECTORS IN FLUCTUATING TARGET ENVIRONMENTS.
  15. Mohamed B. El Mashade (2014). Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers.
  16. M El Mashade (2016). Adaptive Detection Enhancement of Partially-Correlated χ2 Targets in an Environment of Saturated Interference.
  17. José Machado-Fernández,Norelys Mojena-Hernández,Jesús Bacallao-Vidal (2017). Evaluación del desempeño de detectores CFAR.
  18. Mohamed El Mashade (2018). Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ 2 -targets.
  19. M El Mashade (2020). M-Sweeps multi-target analysis of new category of adaptive schemes for detecting χ<sup>2</sup>-fluctuating targets.
  20. Mohamed El Mashade (2021). INHOMOGENEOUS PERFORMANCE EVALUATION OF A NEW METHODOLOGY FOR FLUCTUATING TARGET ADAPTIVE DETECTION.
  21. M El Mashade (2021). Inhomogeneous Analysis of Novel Model of CFAR Approaches to Detect Two-Degrees of Freedom Partially-Correlated χ2-Targets.

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.

Data Availability

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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Enhanced detection of IoT signals using multi-target methods. Improves accuracy in linear fusion and cybersecurity applications.
Issue Cover
GJCST Volume 21 Issue H3
Pg. 19- 42
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
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v1.2

Issue date

January 15, 2022

Language
en
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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.

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

Mohamed Bakry El_Mashade
Mohamed Bakry El_Mashade

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