Wind farm clutter suppression for air surveillance radar based on a combined method of clutter map and K‐SVD algorithm

He, Weikun; Zhang, Xin; Bi, Feng-Hua; Yang, Lei

doi:10.1049/iet-rsn.2019.0399

Cited by 6 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, there is a need to explore new methods and features to avoid this difficulty. Among a large number of works concerning radar discrete clutter suppression or drone separation from discrete clutter, let us recall the work of Martin and Shapiro [9] with a method for discrimination of birds and insects for high-resolution weather radars using a combination of estimated radar cross section and density of targets, the work of Zaugg et al [10] studied the problem of automatic identification of bird targets from insects and ground clutter for radars using wing flapping patterns and support vector classifiers, the work of He et al [11] investigated the problem of wind farm discrete clutter suppression for air surveillance radar based on a clutter map and a generalising algorithm of the K-means clustering process, the work of Jatau et al [12] for detecting birds and insects in the atmosphere using machine learning on patterns of bird and insect echoes based on dual polarisation variables and the study of Liu et al [13] concerned the problem of drone separation from discrete clutter (birds) using motion characteristics. The reduction methods for another type of discrete clutter reflected from buildings were studied in refs [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…[10] studied the problem of automatic identification of bird targets from insects and ground clutter for radars using wing flapping patterns and support vector classifiers, the work of He et al. [11] investigated the problem of wind farm discrete clutter suppression for air surveillance radar based on a clutter map and a generalising algorithm of the K‐means clustering process, the work of Jatau et al. [12] for detecting birds and insects in the atmosphere using machine learning on patterns of bird and insect echoes based on dual polarisation variables and the study of Liu et al.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

Vu Hop,

Cao Quyen,

Van Loi

2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The authors deal with the problem of the design and manufacture of a high‐resolution FMCW radar for drone detection and classification. The difficulties of the problem are discrete clutter reduction and spurious phase noise mitigation. The discrete clutter is due to the reflected signals from land vehicles, birds etc., while the spurious phase noise is inherent in the radar signal due to the phase‐locked loop component and leakage between the transmitting and receiving paths. Both spurious phase noise and clutter will increase the system noise level and hence reduce the probability of detection of small targets such as drones and induce false alarms on the radar screen. In order to reduce discrete clutter, the authors propose a method to separate a drone from discrete clutter based on the design of the radar system parameters for a drone and its propeller detection, target's Doppler dispersion and moving characteristics. For spur mitigation, a method that focuses on the design of the isolation coefficient between transmitting and receiving paths to decrease the power of spurs below the minimum power requirement at the input of the analogue‐to‐digital converter is introduced. The results were applied by the authors to the development and manufacture of a radar with the given specifications for drone detection and classification. Different laboratory and field tests show that the spurs are mitigated and the drones are separated from discrete clutter with a range and accuracy better than the one recently published.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

Vu Hop,

Cao Quyen,

Van Loi

2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…It can be seen that in radar plot processing, how to effectively detect targets from a large amount of clutter and uncertain plots is the key to achieving precise target tracking by radar [3][4][5]. In order to ensure efficient and fast data processing for radar plots, traditional radar plot recognition algorithms usually use binary classification recognition rules to determine whether they are a target or clutter [6][7][8][9]. However, it is wellknown that targets cannot be accurately classified in some cases.…”

Section: Introductionmentioning

confidence: 99%

RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification

Yang,

Zhao,

Shi

2023

Applied Sciences

View full text Add to dashboard Cite

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods.

show abstract

“…Among a large number of works concerning to radar discrete clutter suppression, let us recall the work of Martin and Shapiro (see [9]) with a method for discrimination of birds and insects for high resolution weather radars using a combination of estimated radar cross section and density of targets, the work of Zaugg et al (see [10]) studied a problem of automatic identification of bird targets from insects and ground clutters for radars using wing flapping patterns and support vector classifiers, the work of He et al (see [11]) investigated a problem of wind farm discrete clutter suppression for air surveillance radar based on clutter map and a generalizing algorithm of K-means clustering process, the work of Jatau et al (see [12]) for detecting birds and insects in the atmosphere using machine learning on pattern of bird and insect echoes based on dual polarization variables. The reduction methods for another type of radar discrete clutters reflected from buildings was studied in [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Discrete Clutter Reduction Method for Drone Detection with High Resolution Radars

Hop¹,

Loi²,

Tien³

et al. 2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

The paper deals with a problem of noise reduction for drone detection with high range resolution radars. The considered discrete clutter includes land vehicles, birds, etc. This clutter involves in the radar echo signals and could not be rejected by using classical methods, for example moving target indicator (MTI) and moving target detection (MTD). As a result, this clutter forms the false alarms on the radar screen. To reduce clutter we propose a new method which is based on the target’s Doppler dispersion and moving characteristic. The study is integrated on a drone detection radar and is tested with a variety of drones in different moving scenarios. The test results show that the proposed method achieves a better performance than that one recently published.

show abstract

Wind farm clutter suppression for air surveillance radar based on a combined method of clutter map and K‐SVD algorithm

Cited by 6 publications

References 21 publications

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

A design of a high‐resolution frequency modulated continuous wave radar for drone detection based on spurious phase noise and discrete clutter reduction

RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification

A Discrete Clutter Reduction Method for Drone Detection with High Resolution Radars

Contact Info

Product

Resources

About