Optimal Estimation of High-Dimensional Covariance Matrices with Missing and Noisy Data

The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant studies are based on complete data. This paper studies the optimal estimation of high-dimensional covariance matrices based on missing and noisy sample under the norm. First, the model with sub-Gaussian additive noise is presented. The generalized sample covariance is then modified to define a hard thresholding estimator , and the minimax upper bound is derived. After that, the minimax lower bound is derived, and it is concluded that the estimator presented in this article is rate-optimal. Finally, numerical simulation analysis is performed. The result shows that for missing samples with sub-Gaussian noise, if the true covariance matrix is sparse, the hard thresholding estimator outperforms the traditional estimate method.

Global Attractor of Two-Dimensional Strong Damping KDV Equation and Its Dimension Estimation

Firstly, a priori estimates are obtained for the existence and uniqueness of solutions of two dimensional KDV equations, and prove the existence of the global attractor, finally get the upper bound estimation of the Hausdorff and fractal dimension of attractors.

Examination of the Diagnostic Value and Estimation of the Chaos Phenomenon in Temporomandibular Joint Sounds Using Fractal Dimension

The aim of this study is to estimate the chaos phenomenon in temporomandibular joints (TMJ) sound using fractal dimension (FD),and to examine the diagnostic value of the FD in comparing TMJ sounds produced by 6 asymptomatic and 25 symptomatic TMJ. Multiple mandibular opening and closing cycles recorded were used to calculate the waveform dimension and correlation dimension in the FD. Chaos in the TMJ sounds was estimated by the FD that was saturated with some constant value to an increase of embedding dimension. Results reveal that fractal analysis produces a high degree of reproducibility within,and similarity across subjects,and indicate that both FD values of the asymptomatic TMJ sounds are significantly higher than those of the symptomatic. These findings suggest that chaos is present in TMJ sounds and the difference in the FD is of diagnostic value in evaluation of pathological change in TMJ sound signals.

Variance Estimation for High-Dimensional Varying Index Coefficient Models

This paper studies the re-adjusted cross-validation method and a semiparametric regression model called the varying index coefficient model. We use the profile spline modal estimator method to estimate the coefficients of the parameter part of the Varying Index Coefficient Model (VICM), while the unknown function part uses the B-spline to expand. Moreover, we combine the above two estimation methods under the assumption of high-dimensional data. The results of data simulation and empirical analysis show that for the varying index coefficient model, the re-adjusted cross-validation method is better in terms of accuracy and stability than traditional methods based on ordinary least squares.

Ultra-High Dimensional Feature Selection and Mean Estimation under Missing at Random

Next Generation Sequencing (NGS) provides an effective basis for estimating the survival time of cancer patients, but it also poses the problem of high data dimensionality, in addition to the fact that some patients drop out of the study, making the data missing, so a method for estimating the mean of the response variable with missing values for the ultra-high dimensional datasets is needed. In this paper, we propose a two-stage ultra-high dimensional variable screening method, RF-SIS, based on random forest regression, which effectively solves the problem of estimating missing values due to excessive data dimension. After the dimension reduction process by applying RF-SIS, mean interpolation is executed on the missing responses. The results of the simulated data show that compared with the estimation method of directly deleting missing observations, the estimation results of RF-SIS-MI have significant advantages in terms of the proportion of intervals covered, the average length of intervals, and the average absolute deviation.

Differential-geometry-based multi-dimensional joint position-velocity estimation using Crab pulsar profile distortion

The traditional orbit determination method based on pulsar profile distortion can determine the six elements of the orbit.However,the estimation accuracies of these methods are limited and the computational load of a six-dimensional search is huge.To solve this problem,the differential-geometry-based Multi-dimensional Joint Position-Velocity Estimation(MJPVE)using Crab pulsar profile distortion is proposed in this paper.Firstly,through theoretical analysis,it is found that the pulsar profile distortion caused by the initial state error in some joint positionvelocity directions is very small.In other words,the accuracies of estimation in these directions are very low.Namely,the search dimension can be reduced,which in turn greatly reduces the computational load.Then,we construct the chi-squared function of the pulsar profile with respect to the estimation error in joint position-velocity direction and use differential geometry to find the joint position-velocity directions corresponding to different degrees of distortion.Finally,we utilize the grid search based on directory folding in these joint position-velocity directions corresponding to large degrees of distortion to obtain the joint position-velocity estimation.The experimental results show that compared with the grouping bi-chi-squared inversion method,MJPVE has high precision and extensive navigation information.

Low-Complexity Joint Channel Estimation and Symbol Detection for OFDMA Systems

In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.

Estimation and Prediction of the Condition of the Vehicle Engine Based on the Correlation Dimension

This paper applies the fractal dimension as a characteristic to describe the engine抯 operating condition and its developmental trend. A correlation dimension is one of the quantities that are usually used to characterize a strange attractor. With the operation of the phase space reconstruction, respective correlation dimensions of a series of vibration signals obtained under different conditions are calculated to find the intrinsic relationship between the indicator and the operating condition. The experiment result shows that the correlation dimension is sensitive to the condition evolution and convenient for the identification of abnormal operational states. In advanced prognostic algorithm based on the BP neural network is then applied on the correlation dimensions to predict the short-term running conditions in order to avoid severe faults and realize in-time maintenance. Experimental results are presented to illustrate the proposed methodology.

Joint 2D DOA and Doppler frequency estimation for L-shaped array using compressive sensing

A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.

Cramer-Rao bound of joint estimation of target location and velocity for coherent MIMO radar

The optimal estimation performance of target parameters is studied. First, the general form of Cramer-Rao bound （CRB） for joint estimation of target location and velocity is derived for coherent multiple input multiple output （MIMO） radars. To gain some insight into the behavior of the CRB, the CRB with a set of given orthogonal waveforms is studied as a specific case. Second, a maximum likelihood （ML） estimation algorithm is proposed. The mean square error （MSE） of the ML estimation of target location and velocity is obtained by Monte Carlo simulation and it approaches CRB in the high signal-to-noise ratio （SNR） region.

ADC-GERT network parameter estimation model for mission effectiveness of joint operation system

Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment.With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic,the analog-to-digital converter-graphical evaluation and review technique(ADC-GERT)network parameter estimation model is proposed based on the ADC model and the joint operation system structure.Firstly,analysis of the joint operation system structure and operation process is conducted to build the GERT network,where equipment subsystems are nodes and activities are directed arches.Then the mission effectiveness of equipment subsystems is calculated by the ADC model.The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem,with the ADC-GERT network parameter estimation model constructed.Finally,a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.

Computationally Efficient MUSIC-Based Algorithm for Joint Direction of Arrival (DOA) and Doppler Frequency Estimation in Monostatic MIMO Radar

The problem of joint direction of arrival (DOA) and Doppler frequency estimation in monostatic multiple-input multiple-output (MIMO) radar is studied and a computationally efficient multiple signal classification (CE-MUSIC) algorithm is proposed.Conventional MUSIC algorithm for joint DOA and Doppler frequency estimation requires a large computational cost due to the two dimensional (2D) spectral peak searching.Aiming at this shortcoming,the proposed CE-MUSIC algorithm firstly uses a reduced-dimension transformation to reduce the subspace dimension and then obtains the estimates of DOA and Doppler frequency with only one-dimensional (1D) search.The proposed CE-MUSIC algorithm has much lower computational complexity and very close estimation performance when compared to conventional 2D-MUSIC algorithm.Furthermore,it outperforms estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm.Meanwhile,the mean squared error (MSE) and Cramer-Rao bound (CRB) of joint DOA and Doppler frequency estimation are derived.Detailed simulation results illustrate the validity and improvement of the proposed algorithm.

Joint synchronization estimation based on genetic algorithm for OFDM/OQAM systems

This paper investigates the problem of synchronization for offset quadrature amplitude modulation based orthogonal frequency division multiplexing(OFDM/OQAM) systems based on the genetic algorithm. In order to increase the spectrum efficiency,an improved preamble structure without guard symbols is derived at first. On this basis, instead of deriving the log likelihood function of power spectral density, joint estimation of the symbol timing offset and carrier frequency offset based on the preamble proposed is formulated into a bivariate optimization problem. After that, an improved genetic algorithm is used to find its global optimum solution. Conclusions can be drawn from simulation results that the proposed method has advantages in the joint estimation of synchronization.

Global Attractor and Dimension Estimation for a 2D Generalized Anisotropy Kuramoto-Sivashinsky Equation

In this paper, firstly, some priori estimates are obtained for the existence and uniqueness of solutions of a two dimensional generalized anisotropy Kuramoto-Sivashinsky Equation. Then we prove the existence of the global attractor. Finally, we get the upper bound estimation of the Haus-dorff and fractal dimension of attractor.

Global Attractors and Dimension Estimation of the 2D Generalized MHD System with Extra Force

In this paper, firstly, some priori estimates are obtained for the existence and uniqueness of solu-tions of a two dimensional generalized magnetohydrodynamic (MHD) system. Then the existence of the global attractor is proved. Finally, the upper bound estimation of the Hausdorff and fractal dimension of attractor is got.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival（DOA） and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace（IPNS） to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification（MUSIC） method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

An improved state-of-charge estimation method for sodium-ion battery based on combined correction of voltage and internal resistance

ABSTRACT The accurate state-of-charge(SOC)estimation of sodium-ion batteries is the basis for their efficient application.In this paper,a new SOC estimation method suitable for sodium-ion batteries and their application conditions is proposed,which considers the combination of open circuit voltage(OCV)and internal resistance correction.First,the optimal order of equivalent circuit model is analyzed and selected,and the monotonic and stable mapping relationships between OCV and SOC,as well as between ohmic internal resistance and SOC are determined.Then,a joint estimation algorithm for battery model parameters and SOC is estab-lished,and a joint SOC correction strategy based on OCV and ohmic internal resistance is established.The test results show that OCV correction is reliable when polarization is small,that the ohmic internal resistance correction is reliable when the current fluctuation is large,and that the maximum absolute error of SOC estimation of the proposed method is not more than 2.6%.

A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system

Joint power control has advantages of multi-user detection and power control; and it can combat the multi-access interference and the near-far problem. A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system was designed. Simulation results show that the algorithm can control the power not only quickly but also precisely with a time change. The method is useful for increasing system capacity.

A method of Robust low-angle target height and compound reflection coefficient joint estimation

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method.In this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets.The compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target height.The algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient error.Fi-nally,the experiment and simulation results demonstrate the validity of the proposed method.