OS CFAR Computation Time Reduction Technique to Apply Radar System
                        in Real Time

Kong, Young-Joo; Woo, Seon-Keol; Park, Sungho; Shin, Seungyong; Jang, Youn Hui; Yang, Eunjung

doi:10.5515/kjkiees.2018.29.10.791

Cited by 4 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…스페클 제거 과정을 통해 어느 정도 잡음 및 스페클의 영향이 완화된 영상에서 표적의 가능성이 있는 화소를 추출하기 위해 CFAR 탐지 기법을 이용한다. CFAR 탐지 기법으로는 영상 전체에 고정된 문턱값을 적용하는 FT(fixed-threshold) CFAR 기법과 2차원 커널(kernel)을 이 용하여 주변 화소의 값들을 바탕으로 적응적으로 문턱값 을 설정하는 CA(cell-averaging), OS(ordered statistic) CFAR 기법 등이 있다 [9], [10] . 2D CFAR 기법의 경우 피크(peak) 값 을 찾는 데 매우 효과적이나, 비교적 계산 시간이 오래 걸 려 다수의 표적에 의한 산란점들이 불규칙하게 존재하는 SAR 환경에서는 FT-CFAR 방법이 효과적일 수 있다.…”

unclassified

Principal Component Analysis-Based Dense Target Discrimination Method for Satellite Synthetic Aperture Radar Images

Jeong¹,

Choi²,

Lee³

et al. 2022

J. Korean Inst. Electromagn. Eng. Sci.

View full text Add to dashboard Cite

When detecting targets of interest in satellite synthetic aperture radar (SAR) images, clustering is often required to construct the candidate pixels of an object. In this paper, we propose a new feature using principal component analysis to address the problem of dense targets that cause performance degradation when using the density-based spatial clustering of noise algorithm, which is suitable for the clustering process. Further, simulations are used to analyze the proposed features for different target and clutter clusters extracted from real TerraSAR-X (TSX) images, and the proposed dense target discrimination technique is applied to the TSX images. The simulation results reveal that the proposed feature is effective in distinguishing clusters composed of dense targets, and the target detection performance can be improved by re-separating dense target clusters from actual SAR images.

show abstract

unclassified

Principal Component Analysis-Based Dense Target Discrimination Method for Satellite Synthetic Aperture Radar Images

Jeong¹,

Choi²,

Lee³

et al. 2022

J. Korean Inst. Electromagn. Eng. Sci.

View full text Add to dashboard Cite

show abstract

Multitarget CFAR Detection Method Based on Neural Network for HF Hybrid Sky-Surface Wave Radar in Weibull Clutter Background

Li,

Zhang,

Wang

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

Multi-Target CFAR Detection Method for HF Over-The-Horizon Radar Based on Target Sparse Constraint in Weibull Clutter Background

Zhang

Zheng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the range–Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Our previous research shows that the number of targets belongs to an absolute minority compared to the number of background units. In this paper, we propose a new method for multi-target detection building on the ordered statistics constant false alarm detector (OS-CFAR). The new method fully utilizes the sparse characteristics of the target and uses the idea of introducing regularization processing to eliminate interfering targets, and obtain an estimate of shape parameters for target detection. To further improve the performance of the algorithm, a correction method is proposed for the inaccurate selection of the k value. Upon estimating the distribution parameters, the detection threshold is calculated, and the target’s constant false alarm detection is completed. Simulation and measured data show that our algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions, providing a reliable target detection method.

show abstract

OS CFAR Computation Time Reduction Technique to Apply Radar System in Real Time

Cited by 4 publications

References 4 publications

Principal Component Analysis-Based Dense Target Discrimination Method for Satellite Synthetic Aperture Radar Images

Principal Component Analysis-Based Dense Target Discrimination Method for Satellite Synthetic Aperture Radar Images

Multitarget CFAR Detection Method Based on Neural Network for HF Hybrid Sky-Surface Wave Radar in Weibull Clutter Background

Multi-Target CFAR Detection Method for HF Over-The-Horizon Radar Based on Target Sparse Constraint in Weibull Clutter Background

Contact Info

Product

Resources

About