Radar target detection method based on particle filter theory under correlated non-Gaussian clutter backgrounds

2015 ◽  
Author(s):  
X. Feng ◽  
J. Jiang ◽  
P. Lin ◽  
W.H. Wu
Keyword(s):  
Particle Filter ◽  
Target Detection ◽  
Detection Method ◽  
Radar Target ◽  
Filter Theory ◽  
Non Gaussian

Particle filter initialization in non-linear non-Gaussian radar target tracking

2007 ◽  
Vol 18 (3) ◽  
pp. 491-496 ◽  
Author(s):  
Wang Lian ◽  
Jin Yonggao ◽  
Dai Dingzhang ◽  
Dong Huachun ◽  
Quan Taifan
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Radar Target ◽  
Non Linear ◽  
Non Gaussian

Radar target detection method based on improved YOLOv3

2021 ◽  
Author(s):  
L. Zhongwei ◽  
G. Hongwei ◽  
L. Yingchun ◽  
X. Rongrong ◽  
S. Shuyan ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Radar Target

Radar target detection method based on cross‐correlation singularity power spectrum

2019 ◽  
Vol 13 (5) ◽  
pp. 730-739 ◽  
Author(s):  
Gang Xiong ◽  
Caiping Xi ◽  
Jin He ◽  
Wenxian Yu
Keyword(s):  
Power Spectrum ◽  
Target Detection ◽  
Cross Correlation ◽  
Detection Method ◽  
Radar Target

scholarly journals Target Detection Method Before Tracking based on Particle Filter

2017 ◽  
Vol 107 ◽  
pp. 141-147 ◽  
Author(s):  
Zheng Wang ◽  
Cuiqiong Mo ◽  
Huanyao Dai
Keyword(s):  
Particle Filter ◽  
Target Detection ◽  
Detection Method

scholarly journals PCA-Based Matrix CFAR Detection for Radar Target

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 756
Author(s):  
Zheng Yang ◽  
Yongqiang Cheng ◽  
Hao Wu
Keyword(s):  
False Alarm ◽  
Covariance Matrix ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Radar Target ◽  
Matrix Space ◽  
Cfar Detection ◽  
Dimensional Matrix ◽  
Efficient Detection

In radar target detection, constant false alarm rate (CFAR), which stands for the adaptive threshold adjustment with variation of clutter to maintain the constant probability of false alarm during the detection, plays an important role. Matrix CFAR detection performed on the manifold of Hermitian positive-definite (HPD) covariance matrices is an efficient detection method that is based on information geometry. However, the HPD covariance matrix, which is constructed by a small bunch of pulses, describes the correlations among received data and suffers from severe information redundancy that limits the improvement of detection performance. This paper proposes a Principal Component Analysis (PCA) based matrix CFAR detection method for dealing with the point target detection problems in clutter. The proposed method can not only reduce dimensionality of HPD covariance matrix, but also reduce the redundant information and enhance the distinguishability between target and clutter. We first apply PCA to the cell under test, and construct a transformation matrix to map higher-dimensional matrix space to a lower-dimensional matrix space. Subsequently, the corresponding detection statistics and detection decision on matrix manifold are derived. Meanwhile, the corresponding signal-to-clutter ratio (SCR) is improved. Finally, the simulation experiment and real sea clutter data experiment show that the proposed method can achieve a better detection performance.

Sign in / Sign up

Export Citation Format

Share Document