arXiv subjects

Huiyue Yi

Publications and source records attributed to Huiyue Yi.

5 recordsLinked to original sources

arXiv2025

Enhanced RMT estimator for signal number estimation in the presence of colored noise

The subspace-based techniques are widely utilized in various scientific fields, and they need accurate estimation of the signal subspace dimension. The classic RMT estimator for model order estimation based on random matrix theory assumes that the noise is white Gaussian, and performs poorly in the presence of colored noise with unknown covariance matrix. In the presence of colored noise, the multivariate regression (MV-R) algorithm models the source detection as a multivariate regression problem and infers the model order from the covariance matrix of the residual error. However, the MV-R algorithm requires that the noise is sufficiently weaker than the signal. In order to deal with these problems, this paper proposes a novel signal number estimation algorithm in the presence of colored noise based on the analysis of the behavior of information theoretic criteria. Firstly, a first criterion is defined as the ratio of the current eigenvalue and the mean of the next ones, and its properties is analyzed with respect to the over-modeling and under-modeling. Moreover, a second criterion is designed as the ratio of the current value and the next value of the first criterion, and its properties is analyzed with respect to the over-modeling and under-modeling. Then, a novel enhanced RMT estimator is proposed for signal number estimation by analyzing the detection properties among the signal number estimates obtained by these two criteria, the MV-R estimator and the RMT estimator to sequentially determine whether the eigenvalue being tested is arising from a signal or from noise. Finally, simulation results are presented to illustrate that the proposed enhanced RMT estimator has better estimation performance than the existing methods.

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arXiv2022

Iterative Adaptively Regularized LASSO-ADMM Algorithm for CFAR Estimation of Sparse Signals: IAR-LASSO-ADMM-CFAR Algorithm

The least-absolute shrinkage and selection operator (LASSO) is a regularization technique for estimating sparse signals of interest emerging in various applications and can be efficiently solved via the alternating direction method of multipliers (ADMM), which will be termed as LASSO-ADMM algorithm. The choice of the regularization parameter has significant impact on the performance of LASSO-ADMM algorithm. However, the optimization for the regularization parameter in the existing LASSO-ADMM algorithms has not been solved yet. In order to optimize this regularization parameter, we propose an efficient iterative adaptively regularized LASSO-ADMM (IAR-LASSO-ADMM) algorithm by iteratively updating the regularization parameter in the LASSO-ADMM algorithm. Moreover, a method is designed to iteratively update the regularization parameter by adding an outer iteration to the LASSO-ADMM algorithm. Specifically, at each outer iteration the zero support of the estimate obtained by the inner LASSO-ADMM algorithm is utilized to estimate the noise variance, and the noise variance is utilized to update the threshold according to a pre-defined const false alarm rate (CFAR). Then, the resulting threshold is utilized to update both the non-zero support of the estimate and the regularization parameter, and proceed to the next inner iteration. In addition, a suitable stopping criterion is designed to terminate the outer iteration process to obtain the final non-zero support of the estimate of the sparse measurement signals. The resulting algorithm is termed as IAR-LASSO-ADMM-CFAR algorithm. Finally, simulation results have been presented to show that the proposed IAR-LASSO-ADMM-CFAR algorithm outperforms the conventional LASSO-ADMM algorithm and other existing algorithms in terms of reconstruction accuracy, and its sparsity order estimate is more accurate than the existing algorithms.

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arXiv2020

Source Enumeration via RMT Estimator Based on Linear Shrinkage Estimation of Noise Eigenvalues Using Relatively Few Samples

Estimating the number of signals embedded in noise is a fundamental problem in array signal processing. The classic RMT estimator based on random matrix theory (RMT) tends to under-estimate the number of signals as it does not consider the non-negligible bias term among eigenvalues for finite sample size. Moreover, the RMT estimator suffers from uncertainty in noise variance estimation problem. In order to overcome these problems, we firstly derive a more accurate expression for the distribution of the sample eigenvalues and the bias term among eigenvalues by utilizing the linear shrinkage (LS) estimate of noise sample eigenvalues. Then, we analyze the effect of the bias term among eigenvalues on the estimation performance of the RMT estimator, and derive the increased under-estimation probability of the RMT estimator incurred by this bias term. Based on these results, we propose a novel RMT estimator based on LS estimate of noise eigenvalues (termed as LS-RMT estimator) by incorporating the bias term into the decision criterion of the RMT estimator. As the LS-RMT estimator incorporates this bias term among eigenvalues into the decision criterion of the RMT estimator, it can detect signal eigenvalues immersed in this bias term. Therefore, the LS-RMT estimator can overcome the higher under-estimation probability of the RMT estimator incurred by the bias term among eigenvalues, and also avoids the uncertainty in the noise variance estimation suffered by the RMT estimator as the noise variance is estimated under the assumption that the eigenvalue being tested is arising from noise. Finally, extensive simulation results are presented to show that the proposed LS-RMT estimator outperforms the existing estimators.

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arXiv2019

RMT Estimator with Adaptive Decision Criteria for Estimating the Number of Signals Based on Random Matrix Theory

Estimating the number of signals embedded in noise is a fundamental problem in signal processing. As a classic estimator based on random matrix theory (RMT), the RMT estimator estimates the number of signals via sequentially testing the likelihood of an eigenvalue as arising from a signal or noise for a given over-detection probability. However, it tends to under-estimate the number of signals as weak signal eigenvalues may be immersed in the non-negligible bias term among eigenvalues for finite sample size. In order to solve this problem, we propose an RMT estimator with adaptive decision criterion (termed as RMT-ADC estimator) by adaptively incorporating the bias term into the decision criterion of the RMT estimator. Firstly, we analyze the effect of this bias term among eigenvalues on the estimation performance of the RMT estimator. Then, we derive both the decreased over-estimation probability and the increased under-estimation probability of the RMT estimator incurred by the bias term when assuming the eigenvalue being tested is arising from a signal, and also derive the increased under-estimation probability of the RMT estimator incurred by the bias term when assuming the eigenvalue being tested is arising from noise. Based on these results, the RMT-ADC estimator can adaptively determine whether the noise variance should be estimated under the assumption that the eigenvalue being tested is arising from a signal or from noise, and thus can adaptively select its decision criterion. Moreover, the RMT-ADC estimator can adaptively determine whether the bias term among eigenvalues should be incorporated into the selected decision criterion or not. Therefore, the RMT-ADC estimator can avoid the higher under-estimation probability of the RMT estimator. Finally, simulation results are presented to show that the proposed RMT-ADC estimator significantly outperforms the existing estimators.

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arXiv2012

Joint Doppler frequency shift compensation and data detection method using 2-D unitary ESPRIT algorithm for SIMO-OFDM railway communication systems

In this paper, we present a joint Doppler frequency shift compensation and data detection method using 2-D unitary ESPRIT algorithm for SIMO-OFDM railway communication systems over fast time-varying sparse multipath channels. By creating the spatio-temporal array data matrix utilizing the ISI-free part of the CP (cyclic prefix), we first propose a novel algorithm for obtaining auto-paired joint DOA and Doppler frequency shift estimates of all paths via 2-D unitary ESPRIT algorithm. Thereafter, based on the obtained estimates, a joint Doppler frequency shift compensation and data detection method is developed. This method consists of three parts: (a) the received signal is spatially filtered to get the signal corresponding to each path, and the signal corresponding to each path is compensated for the Doppler frequency shift in time domain, (b) the Doppler frequency shift-compensated signals of all paths are summed together, and (c) the desired information is detected by performing FFT on the summed signal after excluding the CP. Moreover, we prove that the channel matrix becomes time-invariant after Doppler frequency shift compensation and the ICI is effectively avoided. Finally, simulation results are presented to demonstrate the performance of the proposed method and compare it with the conventional method.

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