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Parameter Estimation of Multiple Frequency-Hopping Signals Based on Space-Time-Frequency Analysis by Atomic Norm Soft Thresholding with missing observations

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China Communications 2022年第7期19卷 135-151页

作者： Hongbin Wang Bangning Zhang Heng Wang Binbin Wu Daoxing Guo College of Communications and Engineering Army Engineering UniversityNanjing 210007China

In this paper,we address the problem of multiple frequency-hopping(FH)signal parameters estimation in the presence of random missing observations.A space-time matrix with random missing observations is acquired by a uniform linear array(ULA).We exploit the inherent incomplete data processing capability of atomic norm soft thresholding(AST)to analyze the space-time matrix and complete the accurate estimation of the hopping time and frequency of the received FH *** hopping time is obtained by the sudden changes of the spatial information,which is implemented as the boundary to divide the time domain signal so that each segment of the signal is a superposition of time-invariant multiple ***,the frequency of multiple signal components can be estimated precisely by AST within each *** obtaining the above two parameters of the hopping time and the frequency of signals,the direction of arrival(DOA)can be directly calculated by them,and the network sorting can be *** of simulation show that the proposed method is superior to the existing *** when a large portion of data observations is missing,as the number of array elements increases,the proposed method still achieves acceptable accuracy of multi-FH signal parameters estimation.

关键词： frequency hopping parameter estimation missing observations atomic norm soft thresholding uniform linear array

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How Deep Learning Networks could be Designed to Locate Mineral Deposits

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Journal of Earth Science 2021年第2期32卷 288-292页

作者： Donald A.Singer 10191 N.Blaney Ave. Cupertino 95014CA USA

Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be *** examples of neural networks have mostly been limited to one individual mineral deposit for *** of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other *** trained with only one deposit tend to be academic successes but are not of practical value in exploration for other *** is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any *** examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing *** training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the *** learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for *** this plan,10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.

关键词： porphyry copper training neural networks missing observations

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On Almost Sure Max-limit Theorems of Complete and Incomplete Samples from Stationary Sequences

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Acta Mathematica Sinica,English Series 2011年第7期27卷 1323-1332页

作者： Bin TONG Zuo Xiang PENG Financial Engineering Research Center Shanghai Jiao Tong University Shanghai 200052 P. R. China School of Mathematics and Statistics Southwest University Chongqing 400715 P. R. China

Let Mn denote the partial maximum of a strictly stationary sequence （Xn）. Suppose some of the random variables of （Xn） can be observed and let Mn stand for the maximum of observed random variables from the set {X1,..., Xn}. In this paper, the almost sure limit theorems related to random vector （Mn, Mn） are considered in terms of i.i.d, case. The related results are also extended to weakly dependent stationary Gaussian sequence as its covariance function satisfies some regular conditions.

关键词： Almost sure limit theorem maximum missing observations stationary Caussian sequence

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