Adaptive estimators of output SNR for random channels

Author(s):  
S. Kraut ◽  
L.L. Scharf
2021 ◽  
Vol 11 (4) ◽  
pp. 1509
Author(s):  
Anbang Zhao ◽  
Caigao Zeng ◽  
Juan Hui ◽  
Keren Wang ◽  
Kaiyu Tang

Time reversal (TR) can achieve temporal and spatial focusing by exploiting spatial diversity in complex underwater environments with significant multipath. This property makes TR useful for underwater acoustic (UWA) communications. Conventional TR is realized by performing equal gain combining (EGC) on the single element TR output signals of each element of the vertical receive array (VRA). However, in the actual environment, the signal-to-noise ratio (SNR) and the received noise power of each element are different, which leads to the reduction of the focusing gain. This paper proposes a time reversal maximum ratio combining (TR-MRC) method to process the received signals of the VRA, so that a higher output SNR can be obtained. The theoretical derivation of the TR-MRC weight coefficients indicates that the weight coefficients are only related to the input noise power of each element, and are not affected by the multipath structure. The correctness of the derivation is demonstrated with the experimental data of the long-range UWA communications conducted in the South China Sea. In addition, the experimental results illustrate that compared to the conventional TR, TR-MRC can provide better performance in terms of output SNR and bit error rate (BER) in UWA communications.


2014 ◽  
Vol 50 (12) ◽  
pp. 889-891 ◽  
Author(s):  
Biho Kim ◽  
Yunil Hwang ◽  
Hyung‐Min Park

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Mark J. van der Laan ◽  
Richard J. C. M. Starmans

This outlook paper reviews the research of van der Laan’s group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming at only relying on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of uncertainty in order to make sound statistical conclusions. We also provide a philosophical historical perspective on Targeted Learning, also relating it to the new developments in Big Data. We conclude with some remarks explaining the immediate relevance of Targeted Learning to the current Big Data movement.


1988 ◽  
Vol 21 (10) ◽  
pp. 21-26 ◽  
Author(s):  
R.R. Bitmead ◽  
B.D.O. Anderson ◽  
Lei Guo

2012 ◽  
Vol 41 (8) ◽  
pp. 962-966
Author(s):  
崔东旭 CUI Dong-xu ◽  
郑少成 ZHENG Shao-cheng ◽  
邱亚峰 QIU Ya-feng ◽  
钱芸生 QIAN Yun-sheng ◽  
常本康 CHANG Ben-kang

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