Interference control in sliding window detection processes using a Bayesian approach

Weinberg, Graham V.

doi:10.1016/j.dsp.2020.102658

Cited by 13 publications

(3 citation statements)

References 22 publications

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“…Since the threshold

is determined according to

[ 7 ],

is set to 3. Based on the three sliding window scales of 16, 24 and 32 commonly used in marine radar target detection [ 5 , 6 ], the sliding window scale N of WL-CFAR is set to these three sizes, and then a large number of comparisons are made by using experimental data. The results show that when the sliding window scale N is 32, the WL-CFAR detector has the best detection effect on the experimental data.…”

Section: Resultsmentioning

confidence: 99%

“…TM-CFAR improves its detection performance in uniform environment by deleting a certain number of minimum values and maximum values in the sorted reference unit gray value sequence. In the meantime, the Bayesian method was introduced to improve the robustness of the CFAR detector [ 6 ]. A Bayesian detector reduces the influence of the interfering targets by modeling and removing them.…”

Section: Introductionmentioning

confidence: 99%

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Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Wen

Wei

2022

Entropy

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In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.

show abstract

“…Since the threshold

is determined according to

[ 7 ],

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Wen

Wei

2022

Entropy

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show abstract

“…It will replace the previous manual monitoring or fixed-threshold detection methods, whose thresholds can be dynamically adjusted in real time according to the amplitude of the noise level, thus achieving a balance between false alarm probability and detection probability. To achieve this goal, the constant false alarm rate detector 13 in radar detection theory is used for the real-time detection of pipeline leakage signals. Constant false alarm rate (CFAR) detector is an adaptive threshold method that detects the target with a constant false alarm probability level, which eliminates the problem of threshold failure due to noise level variations.…”

Section: Introductionmentioning

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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A Bayesian CFAR detector for interference control in Weibull clutter

Zhang

Xie

Zhou

2020

Digital Signal Processing

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Interference control in sliding window detection processes using a Bayesian approach

Cited by 13 publications

References 22 publications

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

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

A Bayesian CFAR detector for interference control in Weibull clutter

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