Weighted likelihood CFAR detection for Weibull background

Zhang, Baiqiang; Zhou, Jie; Xie, Junhao; Zhou, Wei

doi:10.1016/j.dsp.2021.103079

Cited by 20 publications

(7 citation statements)

References 33 publications

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“…Next, WL-CFAR is used to detect the target in the original image and the sea clutter suppressed image. The proposer of WL-CFAR provides the selection basis of each parameter of WL-CFAR in [ 7 ]. The value of the shape parameter C of the Weibull distribution is set according to the distribution characteristics of the clutter data, usually between 1 and 2.…”

Section: Resultsmentioning

confidence: 99%

“…In order to achieve around 16 false alarm points in each image and stabilize the detection rate at a high level,

is set to 0.001. 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.…”

Section: Resultsmentioning

confidence: 99%

“…The value of robustness measurement parameter

is proved to be the best set between 0.1 and 0.9. Moreover the smaller the

value, the better the detector’s robustness [ 7 ]. After other parameters are determined, the value of

is set to 0.1, 0.2,..., 0.9, respectively, and WL-CFAR with the set

value is used to detect the target of our experimental data.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, weighted likelihood constant false alarm rate(WL-CFAR) [ 7 ] is used to detect targets in sea clutter suppressed image sequence. It estimates the scale parameters of Weibull distribution by maximizing the weighted log likelihood function.…”

Section: Sea Clutter Suppression and Target Detection Algorithmmentioning

confidence: 99%

“…However, such detectors require appropriate prior information and are computationally complex. Recently, a CFAR based on weighted likelihood (WL) estimator for Weibull clutter with known shape parameter is proposed [ 7 ]. WL-CFAR estimates the scale parameters of Weibull distribution by maximizing the weighted log likelihood function.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Section: Resultsmentioning

confidence: 99%

“…In order to achieve around 16 false alarm points in each image and stabilize the detection rate at a high level,

is set to 0.001. Since the threshold

is determined according to

[ 7 ],

Section: Resultsmentioning

confidence: 99%

“…The value of robustness measurement parameter

is proved to be the best set between 0.1 and 0.9. Moreover the smaller the

value, the better the detector’s robustness [ 7 ]. After other parameters are determined, the value of

is set to 0.1, 0.2,..., 0.9, respectively, and WL-CFAR with the set

value is used to detect the target of our experimental data.…”

Section: Resultsmentioning

confidence: 99%

Section: Sea Clutter Suppression and Target Detection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Novel Method for Radar Echo Target Detection

Chen,

Pi,

Wang

et al. 2023

Lecture Notes in Computer Science

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An improved CFAR algorithm for multiple environmental conditions

Rihan,

Nossair,

Mubarak

2024

SIViP

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The primary objective of radar digital signal processing is to detect and identify targets in complicated situations, such as those involving clutter or several closely positioned targets. The constant false alarm rate (CFAR) method is more effective for target recognition and has better control over the false alarm rate. Many studies have been conducted on the design of CFAR, but previous CFAR algorithms have not been effective in all or most environmental fields and target scenarios. In this study, an algorithm called Censored Mean Clutter Map CFAR (CM-CM CFAR) has been developed and tested for various environmental conditions. When compared to a fixed false alarm rate, the suggested CFAR algorithm’s Monte Carlo simulation results demonstrated a high detection probability in a variety of environments. This work designs a real-time CM-CM CFAR processor using field-programmable gate array (FPGA) technologies. Xilinx ARTIX 7 FPGA technology is used to develop and map a scalable parallel framework. Consequently, the implementation required 23,741 LUTs and 1825 FF. It is verified that the complexity and operating speed of the suggested CFAR processor are extremely appropriate for real-time implementation when compared to the results of the previously proposed FPGA implementation.

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Weighted likelihood CFAR detection for Weibull background

Cited by 20 publications

References 33 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

Novel Method for Radar Echo Target Detection

An improved CFAR algorithm for multiple environmental conditions

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