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  1. Book ; Online: Source Enumeration via RMT Estimator Based on Linear Shrinkage Estimation of Noise Eigenvalues Using Relatively Few Samples

    Yi, Huiyue

    2020  

    Abstract: 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 ...

    Abstract 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.

    Comment: 15 pages, 7 figures
    Keywords Computer Science - Information Theory
    Subject code 519
    Publishing date 2020-10-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Enhanced Root-MUSIC Algorithm Based on Matrix Reconstruction for Frequency Estimation.

    Zhu, Yingjie / Zhang, Wuxiong / Yi, Huiyue / Xu, Hui

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 4

    Abstract: In recent years, frequency-modulated continuous wave (FMCW) radar has been widely used in automatic driving, settlement monitoring and other fields. The range accuracy is determined by the estimation of the signal beat frequency. The existing algorithms ... ...

    Abstract In recent years, frequency-modulated continuous wave (FMCW) radar has been widely used in automatic driving, settlement monitoring and other fields. The range accuracy is determined by the estimation of the signal beat frequency. The existing algorithms are unable to distinguish between signal components with similar frequencies. To address this problem, this study proposed an enhanced root-MUSIC algorithm based on matrix reconstruction. Firstly, based on the sparsity of a singular value vector, a convex optimization problem was formulated to identify a singular value vector. Two algorithms were proposed to solve the convex optimization problem according to whether the standard deviation of noise needed to be estimated, from which an optimized singular value vector was obtained. Then, a signal matrix was reconstructed using an optimized singular value vector, and the Hankel structure of the signal matrix was restored by utilizing the properties of the Hankel matrix. Finally, the conventional root-MUSIC algorithm was utilized to estimate the signal beat frequency. The simulation results showed that the proposed algorithm improved the frequency resolution of multi-frequency signals in a noisy environment, which is beneficial to improve the multi-target range accuracy and resolution capabilities of FMCW radar.
    Language English
    Publishing date 2023-02-06
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23041829
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Iterative Adaptively Regularized LASSO-ADMM Algorithm for CFAR Estimation of Sparse Signals

    Yi, Huiyue / Xu, Yan / Zhang, Wuxiong / Xu, Hui

    IAR-LASSO-ADMM-CFAR Algorithm

    2022  

    Abstract: 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), ...

    Abstract 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.

    Comment: 8 pages, 2 figures
    Keywords Computer Science - Information Theory ; Electrical Engineering and Systems Science - Signal Processing
    Subject code 519
    Publishing date 2022-08-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: RMT Estimator with Adaptive Decision Criteria for Estimating the Number of Signals Based on Random Matrix Theory

    Yi, Huiyue

    2014  

    Abstract: 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 ... ...

    Abstract 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.

    Comment: 16 pages, 10 figures
    Keywords Computer Science - Information Theory ; Statistics - Methodology
    Subject code 519
    Publishing date 2014-05-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Book ; Online: Joint Doppler frequency shift compensation and data detection method using 2-D unitary ESPRIT algorithm for SIMO-OFDM railway communication systems

    Yi, Huiyue

    2012  

    Abstract: 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- ... ...

    Abstract 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.

    Comment: 25 pages, 5 figures
    Keywords Computer Science - Information Theory
    Subject code 621
    Publishing date 2012-10-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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