Performance Comparison of Wideband Radar Target Detection Methods

2014 ◽  
Vol 971-973 ◽  
pp. 1680-1683
Author(s):  
Miao He ◽  
Li Yu Tian ◽  
Xiong Jun Fu ◽  
Yun Chen Jiang

In wideband radar situation, target-spread and all scattering points back wave could be considered as the pulse train of random parameters. The wideband radar target and built the related model. Then it gave two methods of target detection, one is Energy Accumulation and the other is the IPTRP. It also presented the simulation result of these two methods performance curves. It showed that the IPTRP improved by more than 3dB in the same SNR.

2004 ◽  
Author(s):  
Atindra K. Mitra ◽  
Thomas L. Lewis ◽  
James P. LaRue ◽  
Arnab K. Shaw

2013 ◽  
Vol 5 (5) ◽  
pp. 1528-1532 ◽  
Author(s):  
Xiandong Meng ◽  
Ganzhong Feng ◽  
Hua Xue ◽  
Zhiming He

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fangzhou Wang ◽  
Pu Wang ◽  
Xin Zhang ◽  
Hongbin Li ◽  
Braham Himed

2021 ◽  
Vol 13 (13) ◽  
pp. 2558
Author(s):  
Lei Yu ◽  
Haoyu Wu ◽  
Zhi Zhong ◽  
Liying Zheng ◽  
Qiuyue Deng ◽  
...  

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.


Entropy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 256 ◽  
Author(s):  
Xiaoqiang Hua ◽  
Haiyan Fan ◽  
Yongqiang Cheng ◽  
Hongqiang Wang ◽  
Yuliang Qin

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