Outliers-Robust CFAR Detector of Gaussian Clutter Based on the Truncated-Maximum-Likelihood- Estimator in SAR Imagery

Ai, Jiaqiu; Luo, Qiwu; Yang, Xuezhi; Yin, Zhiping; Xu, Hao

doi:10.1109/tits.2019.2911692

Cited by 63 publications

(39 citation statements)

References 34 publications

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“…In the above theoretical analysis of T , the Duffing equation shown in (10) does not consider the interference of the ground clutter. Next, the anti-clutter performance of T is tested with the Duffing detection system shown in (13). A group of simulations are designed, which are divided into the following two cases.…”

Section: Obviously Jmentioning

confidence: 99%

“…In conclusion, when using the Duffing detection system shown in (13) to classify FOD and false alarms, only the detection statistic T needs to be calculated. If…”

Section: Obviously Jmentioning

confidence: 99%

“…According to , 1, 2... The fourth-order Runge-Kutta method is used to solve the Duffing equation array shown in (13), the initial value is set as 1 x  , 1 y  and the step size is set as 1/ s f , where s f is the sampling frequency. Calculate the detection statistic i T to each i D .…”

Section: The Detailed Flow Of Proposed Algorithmmentioning

confidence: 99%

“…In recent years, some improved CFAR methods have been proposed in different fields. Outlier robust CFAR [13] uses the adaptively threshold to effectively removed the highintensity outliers and maintained real clutter samples. Bilateral-trimmed-statistics-based CFAR [14] uses the depth adaptive bilateral threshold to remove the both high and low intensity outliers, and greatly maintain the real clutter samples.…”

Section: Introductionmentioning

confidence: 99%

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A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

et al. 2022

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The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.INDEX TERMS Foreign objects debris (FOD), millimeter-wave (MMW) radar, false alarm elimination, Duffing oscillator, quantitative detection method.

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Section: Obviously Jmentioning

confidence: 99%

“…In conclusion, when using the Duffing detection system shown in (13) to classify FOD and false alarms, only the detection statistic T needs to be calculated. If…”

Section: Obviously Jmentioning

confidence: 99%

Section: The Detailed Flow Of Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

et al. 2022

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“…So far, many traditional SAR ship detection methods have been proposed, e.g., global thresholdbased [36][37][38], constant false alarm ratio (CFAR)-based [39][40][41], generalized likelihood ratio test (GLRT)-based [42][43][44], transformation domain-based [45][46][47], visual saliency-based [48][49][50], super-pixel segmentation-based [51][52][53], and auxiliary feature-based (e.g., ship-wake) [54][55][56], all of which obtained modest results in specific backgrounds, but these methods always extract ship features by hand-designed means, leading to complexity in computation, weakness in generalization, and trouble in manual feature extraction [1,4]. Moreover, as ship wakes do not exist all the time, and their features are not as obvious as ship targets, the research on the detection of ship wakes is not extensive [13].…”

Section: Introductionmentioning

confidence: 99%

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Zhang

et al. 2020

Remote Sensing

174

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Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology. LS-SSDD-v1.0 is available at https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN.

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Design of a Drone Platform for Sensor Fusion Data Acquisition

Safa,

Keuninckx,

Gielen

et al. 2024

Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems

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Outliers-Robust CFAR Detector of Gaussian Clutter Based on the Truncated-Maximum-Likelihood- Estimator in SAR Imagery

Cited by 63 publications

References 34 publications

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

A False Alarm Elimination Algorithm of Foreign Objects Debris Detection Based on Duffing Oscillator

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Design of a Drone Platform for Sensor Fusion Data Acquisition

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