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

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CSTIT0888Y

Enhanced detection of IoT signals using multi-target methods. Improves accuracy in linear fusion and cybersecurity applications.

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

Mohamed Bakry El_Mashade
Mohamed Bakry El_Mashade
DOI

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.

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

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.

Mohamed Bakry El_Mashade
Mohamed Bakry El_Mashade

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Mohamed Bakry El_Mashade. 2022. “. Global Journal of Computer Science and Technology – H: Information & Technology GJCST-H Volume 21 (GJCST Volume 21 Issue H3): .

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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST Volume 21 Issue H3
Pg. 19- 42
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GJCST-H Classification: I.5.1
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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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