CA-CFAR Performance in K-Distributed Sea Clutter With Fully Correlated Texture

Medeiros, Diego Silva; García, Fernando Darío Almeida; Machado, Renato; Filho, José Cândido Silveira Santos; Saotome, Osamu

doi:10.1109/lgrs.2023.3238169

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…The traditional CFAR detection is performed by selecting training cells on the both left and right sides of the CUT 23 , 24 . However, in this work, the pipeline leakage occurs instantaneously.…”

Section: Pipeline Leak Detection Based On Constant False Alarm Rate D...mentioning

confidence: 99%

Constant false alarm rate detection of pipeline leakage based on acoustic sensors

An,

Huang,

2023

Sci Rep

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During the transportation of oil and gas pipelines, there are many potential factors that can lead to pipeline leakage with serious consequences, making automatic and real-time pipeline leakage detection urgent. In response to the inconvenience of manual detection, constant false alarm rate (CFAR) detection technique in radar target detection theory is introduced for pipeline leakage detection based on acoustic signals. In this paper, an automatic pipeline leakage detection algorithm based on an improved CFAR detector is proposed. The improved CFAR detection is executed after pre-processing the acoustic signals so as to adaptively set the detection threshold to achieve the purpose of automatic detection of pipeline leakage incidents. A simulated leakage test of a real pipeline is used for validation, and the proposed method achieves detection accuracies of 84.6%, 97.7%, and 98% for different leakage diameter settings, i.e., 5 mm, 7 mm, and 10 mm leak hole diameters, respectively, with an overall accuracy of 94.1%, while the false alarm rates are 3.3%, 0.7%, and 0, respectively, as well as an overall of 1.2%. The results of experimental data based on real scenarios demonstrate the effectiveness of the proposed method.

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Section: Pipeline Leak Detection Based On Constant False Alarm Rate D...mentioning

confidence: 99%

Constant false alarm rate detection of pipeline leakage based on acoustic sensors

An,

Huang,

2023

Sci Rep

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“…The model in [5] considers a Swerling II (exponentially distributed) target and a K-distributed clutter with fully correlated texture in a square-law detector. The received complex baseband signals follow a binary hypothesis test…”

Section: B Ca-cfar Detection For K-distributed Cluttermentioning

confidence: 99%

“…In this context, the cell averaging constant false alarm rate (CA-CFAR) is one of the most traditional detection techniques in the radar field, as it achieves a good target detection rate while maintaining false alarms at constant and acceptable levels [4]. Recently, the theoretical performance of the CA-CFAR, considering a K-distributed clutter scenario, was derived in terms of low computational cost closed-form expressions [5]. However, in HFSWR, as the sea clutter has a strong non-homogeneous statistical behavior, the classical CA-CFAR technique usually fails to guarantee the expected theoretical performance, as already seen in the Weibull-distributed clutter scenario [6].…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Surface Wave Radar Performance Analysis for CA-CFAR Algorithm in K-distributed Clutter

Medeiros,

Costa,

Mippo

et al. 2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This paper analyzes the performance of a cellaveraging constant false alarm rate (CA-CFAR) algorithm designed for K-distributed clutter in High-Frequency Surface Wave Radar applications (HFSWR). Due to complex scattering mechanisms, the clutter returns in the range-Doppler spectrum are highly non-homogeneous. This study compares a closed-form probability of detection of the K-distribution CA-CFAR to a numerical simulation of sea clutter based on a physical sea radar cross-section (RCS) model, which considers wind conditions and operating parameters. The simulation shows that the closedform analytical expression acts as an upper bound for detector performance, which is degraded in practice by the strong peaks of the clutter power.

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“…The CFAR algorithm can be optimized for different background noise models, such as Gaussian white noise. However, if the noise model is inaccurate or imperfect, the performance of the algorithm may be affected [8][9][10]. CFAR algorithm is mainly used to detect target signals in background noise [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

2024

Teh. vjesn.

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It takes a long time to detect target information in noisy radar information and reduce the probability of false alarm. Therefore, it has become a research direction to reduce the probability of false alarm and the time of effective target detection. This paper introduces a new method to reduce the occurrence of false alarm in non-uniform environment and improve the efficiency of target detection. The proposed method involves a faster and more stable method that involves preprocessing the data set, splitting it into smaller parts, and utilizing a KTH minimum value M determined by an ordered statistics class constant false alarm detection algorithm. Each data point in the small segment is then compared to M, anything above M is classified as a target, and anything below M is ignored as clutter. Then ESVI-CFAR detection was performed on the selected target to obtain the final detection result. Experimental results show that the proposed method is superior to the traditional VI-CFAR and has better target detection performance.

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CA-CFAR Performance in K-Distributed Sea Clutter With Fully Correlated Texture

Cited by 13 publications

References 33 publications

Constant false alarm rate detection of pipeline leakage based on acoustic sensors

Constant false alarm rate detection of pipeline leakage based on acoustic sensors

High-Frequency Surface Wave Radar Performance Analysis for CA-CFAR Algorithm in K-distributed Clutter

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

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