Constant false alarm rate detection method in mixed Weibull distribution sea clutter

Mbouombouo Mboungam, Abdel Hamid; Zhi, Yongfeng; Fonzeu Monguen, Cedric Karel

doi:10.1016/j.dsp.2024.104494

Digital Signal Processing

2024

DOI: 10.1016/j.dsp.2024.104494

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Constant false alarm rate detection method in mixed Weibull distribution sea clutter

Abdel Hamid Mbouombouo Mboungam,

Yongfeng Zhi,

Cedric Karel Fonzeu Monguen

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Cited by 4 publications

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Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

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et al. 2024

Digital Signal Processing

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Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

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et al. 2024

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Intelligent Maritime Radar Target Detection With Partial Annotation via Progressive Learning

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Zang

et al. 2024

IEEE Sensors J.

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Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data

Wang

2024

Applied Mathematics and Nonlinear Sciences

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In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long working times, high complexity, and high false detection rates. This paper proposes a new type of sensor and an algorithm for detecting anomalies in sensors based on Flink. The sensor network senses the monitoring targets in the monitoring area in real time and transmits them to the end users through wireless communication. Then, the fusion rule for target detection in sensor networks is studied. Then, the detection algorithm is computed using Flink and predicted using a sliding window and ARIMA model on the Flink platform. Then, the confidence intervals of the prediction results are calculated, and the outliers are evaluated. Finally, the K-Means++ algorithm is used for clustering and probability assessment of previously obtained outliers. Then, compared with traditional sensors, comparative analysis shows that the proposed method in this paper has some advantages in terms of both sensitivity (S) and quality factor (Q). The value of S is more than 320 than the 2D PhC sensor array. The value of Q is more than 5295 than the 2D PhC sensor array. This paper’s new sensor detection method is more diverse than the traditional method. The detection time is reduced. The detection accuracy is also improved compared to the traditional method. It can be summarized by comparison that the new sensor in this paper is more excellent than the traditional method of detection.

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Constant false alarm rate detection method in mixed Weibull distribution sea clutter

Cited by 4 publications

References 23 publications

Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

Mixture texture model with weighted generalized inverse Gaussian distribution for target detection

Intelligent Maritime Radar Target Detection With Partial Annotation via Progressive Learning

Research on the Development of New Sensors and Sensor Detection Technology in the Context of Big Data

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