scholarly journals Mono-pulse radar angle estimation algorithm under low signal-to-noise ratio

2021 ◽  
Vol 1914 (1) ◽  
pp. 012042
Author(s):  
Wenhao Dong ◽  
Jingyi Wang ◽  
Zhiyong Song
2015 ◽  
Vol 9 (14) ◽  
pp. 1788-1792 ◽  
Author(s):  
Huaizong Shao ◽  
Wuling Liu ◽  
Di Wu ◽  
Xiaoli Chu ◽  
Yang Li

2020 ◽  
Vol 25 (2) ◽  
pp. 253-258
Author(s):  
Baohai Yang ◽  
Quanhui Ren ◽  
Haisheng Li ◽  
Junkang Song

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 80
Author(s):  
Hun Im ◽  
Deok Lim ◽  
Sang Lee

In order to estimate the roll angle of a rotating vehicle, an enhanced rotation locked loop (RLL) algorithm is proposed in this paper. The RLL algorithm estimates the roll angle by using the property that the power of the GPS signal measured at the receiver of a rotating vehicle changes periodically. However, in case the received GPS power is decreased, the performance of the conventional RLL algorithm degrades, or it cannot estimate the roll angle anymore, therefore, for operating the RLL algorithm in a weak signal environment, this paper designs a method to increase the signal-to-noise ratio (SNR) by overlapping multiple GPS signals’ correlator outputs and a method to compensate the decreased response of a rotation discriminator at low-signal strength. Through computer simulations, the performance of the proposed algorithm is verified and it is shown that the roll angle can be estimated stably even at a weak signal environment down to 29 dB–Hz of C/N0.


2012 ◽  
Vol 58 (4) ◽  
pp. 603-608 ◽  
Author(s):  
Ayesha Ijaz ◽  
Adegbenga B. Awoseyila ◽  
Barry G. Evans

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaojuan Xie ◽  
Shengliang Peng ◽  
Xi Yang

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.


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