scholarly journals An Anti-FOD Method Based on CA-CM-CFAR for MMW Radar in Complex Clutter Background

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1635 ◽  
Author(s):  
Xiaoqi Yang ◽  
Kai Huo ◽  
Jianwei Su ◽  
Xinyu Zhang ◽  
Weidong Jiang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Millimeter Wave ◽  
Radar System ◽  
Experimental Results ◽  
Foreign Object ◽  
Constant False Alarm Rate ◽  
Test Statistic ◽  
Homogeneous Background ◽  
Distributed Test

Traditional constant false alarm rate (CFAR) methods have shown their potential for foreign object debris (FOD) indication. However, the performance of these methods would deteriorate under the complex clutter background in airport scenes. This paper presents a threshold-improved approach based on the cell-averaging clutter-map (CA-CM-) CFAR and tests it on a millimeter-wave (MMW) radar system. Clutter cases are first classified with variability indexes (VIs). In homogeneous background, the threshold is calculated by the student-t-distributed test statistic; under the discontinuous clutter conditions, the threshold is modified according to current VI conditions, in order to address the performance decrease caused by extended clutter edges. Experimental results verify that the chosen targets can be indicated by the t-distributed threshold in homogeneous background. Moreover, effective detection of the obscured targets could also be achieved with significant detectability improvement at extended clutter edges.

Detection Performance of Several Nonparametric Detectors under K-Distributed Clutter

2012 ◽  
Vol 433-440 ◽  
pp. 6417-6421
Author(s):  
Fu Yong Qu ◽  
Xiang Wei Meng
Keyword(s):  
False Alarm ◽  
False Alarm Probability ◽  
Radar System ◽  
Constant False Alarm Rate ◽  
Multiple Targets ◽  
Input Noise ◽  
Simulation Results ◽  
Homogeneous Background ◽  
Detector Parameter ◽  
Parameter Values

Because of nonparametric detectors’ ability of ensuring constant false alarm rate (CFAR) for a wide class of input noise distributions and engineering implementation simply, much efforts have been directed towards the study of nonparametric methods of signal detection. This paper deals with a comparative analysis of nonparametric detectors-GS, MW, Savage detector under K-distributed clutter in homogeneous and nonhomogeneous background caused by multiple targets and clutter edge. Some results of detection probability versus signal-to-clutter ratio (SCR) are presented in curves for different detector parameter values in homogeneous and multiple targets background. And the ability to control the false alarm probability for the three nonparametric detectors is presented in table. The simulation results show that S detector performs robustly in homogeneous background and clutter edge background, and can tolerate more interfering targets through increasing the number of reference cells and pulse sweeps. Therefore as a compromise solution, S detector with moderate parameters can be used in actual radar system.

scholarly journals Design and Implementation of a New CFAR Based on Weighted and Statistical Algorithms

2020 ◽  
Author(s):  
Thamir Saeed ◽  
Gufran Hatem ◽  
Jafar Abdul Sadah ◽  
Hadi Ziboon
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Simulation Analysis ◽  
Adaptive Threshold ◽  
Radar System ◽  
Constant False Alarm Rate ◽  
Receiver Side ◽  
Design And Implementation ◽  
Statistical Algorithms

In the radar system, detection represents a basic and important stage in the receiver side. The detection process is based on the thresholding criteria; two philosophies of this criteria, constant and adaptive threshold. The constant threshold is simple in design, but it has a mis-detection and does not control the false alarm rate. As for the adaptive threshold, it is powerful in target detection, and better control of the false alarm rate, where it is called Constant False Alarm Rate (CFAR). Lots of research in the CFAR design, but the gap in the previous works is that there is no CFAR algorithm can be working with all or most environmental fields and all or most target situations.In this paper, The CFAR, which can work with the most environment and most of the target situations, has been presented. The producing the design and implementation of the new practical CFAR processor is presented. Where, the new CFAR is a combination of the properties of three different CFAR algorithm (CA, OSGO, and OSSO), and from two different families; averaging and statistical. Where it has overperformed of it's is 97.25% for simulation and 96.25% for the implementable version for different target situations. The simulation analysis is made by using Matlab 2015, while the implementation is done by using Xilinx Spartan 700 3a.

scholarly journals A STUDY OF UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND GAUSSIAN MIXTURE MODEL USING POLSAR SAR DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 965-970
Author(s):  
Y. Yang ◽  
W. Liu
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Constant False Alarm Rate ◽  
Test Statistic ◽  
Polarimetric Sar ◽  
Difference Image

To solve the problems of existing method of change detection using fully polarimetric SAR which not takes full advantage of polarimetric information and the result of false alarm rate of which is high, a method is proposed based on test statistic and Gaussian mixture model in this paper. In the case of the flood disaster in Wuhan city in 2016, difference image is obtained by the likelihoodratio parameter which is built using coherency matrix C3 or covariance matrix T3 of fully polarimetric SAR based on test statistic, and it becomes a reality that the change information is automatic extracted by the parameter of Gaussian mixture model (GMM) of difference image based on the expectation maximization (EM) iterative algorithm. The experimental results show that the overall accuracy of change detection results can be improved and false alarm rate can be reduced using this method by comparison with traditional constant false alarm rate (CFAR) method. Thus the validity and feasibility of the method is demonstrated.

A Constant False Alarm Rate-(CFAR) Based Change Detection Approach to Helicopter Diagnostics

2008 ◽  
Author(s):  
Kenneth Ranney ◽  
Hiralal Khatri ◽  
Jerry Silvious ◽  
Kwok Tom ◽  
Romeo del Rosario
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Constant False Alarm Rate ◽  
Detection Approach

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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