基于时间序列的竞技水平估计算法

体育竞技中,选手水平随时间变化,True Skill-T算法能估计选手时间序列上的竞技水平。文中针对围棋比赛的特点对其进行简化和修改,实现了True Skill Go-Year算法。为研究算法在实际排名应用上的可行性,分别基于真实的围棋比赛数据和仿真数据,考察了算法对数据的适应能力。实验结果表明True Skill-T算法排名结果符合领域常识,具备实际应用的性能。     

Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue

In various applications from radar processing to mobile communication systems based on CDMA or OFDM, M-AR multichannel processes are often considered and may be combined with Kalman filtering. However, the estimations of the M-AR parameter matrices and the autocorrelation matrices of the additive noise and the driving process from noisy observations are key problems to be addressed. In this paper, we suggest solving them as an errors-in-variables issue. In that case, the noisy-observation autocorrelation matrix compensated by a specific diagonal block matrix and whose kernel is defined by the M-AR parameter matrices must be positive semi-definite. Hence, the parameter estimation consists in searching every diagonal block matrix that satisfies this property, in reiterating this search for a higher model order and then in extracting the solution that belongs to both sets. A comparative study is then carried out with existing methods including those based on the Extended Kalman Filter (EKF) and the Sigma-Point Kalman Filters (SPKF). It illustrates the relevance and advantages of the proposed approaches.     

Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation

Based on an asymptotic analysis of the contraction mapping (CM) method of Li and Kedem (1993), a bandwidth shrinkage rule is proposed for fast and accurate estimation of the frequencies of multiple sinusoids from noisy measurements. The CM frequency estimates are defined as the fixed points of a contractive mapping formed by the lag-one autocorrelation coefficient calculated from the output. of a parametric filter applied to the observed time series. With bandwidth parameters judiciously chosen according to the asymptotic analysis, the algorithm is shown to be able to accommodate possibly poor initial values of precision O(n/sup -1/3/) and converge to a final estimate whose accuracy is arbitrarily close to O(n/sup -3/2/), the optimal error rate for frequency estimation under the Gaussian assumption. The total computational complexity of the algorithm is shown to be O(n log n), which is comparable to that of n-point fast Fourier transform (FFT). A novelty in the asymptotic analysis is that it accommodates closely spaced frequencies by allowing not only the filter bandwidth but also the frequency separation to be functions of the sample size n. This enables an assessment of the accuracy of the frequency estimates for given bandwidths and initial values in situations where some or all of the frequencies are close to each other.     

An iterative L1-based image restoration algorithm with an adaptive parameter estimation

Regularization methods for the solution of ill-posed inverse problems can be successfully applied if a right estimation of the regularization parameter is known. In this paper, we consider the L(1)-regularized image deblurring problem and evaluate its solution using the iterative forward-backward splitting method. Based on this approach, we propose a new adaptive rule for the estimation of the regularization parameter that, at each iteration, dynamically updates the parameter value, following the evolution of the objective functional. The iterative algorithm automatically stops, without requiring any assumption about the perturbation process, when the parameter has reached a seemingly near optimal value. In spite of the fact that the optimality of this value has not yet been theoretically proved, a large number of numerical experiments confirm that the proposed rule yields restoration results competitive with those of the best state-of-the-art algorithms.     

A new method of nonstationary time series analysis based oninhomogeneous AR equation

We propose a new model for nonstationary time series analysis. The model is of a noise-contaminated signal of an AR system excited by a sequence of an input signal represented in terms of orthogonal functions. We also propose an algorithm that enables us to estimate parameters of the AR part and the input signal simultaneously. The models are finally evaluated by testing the recovery of an output signal. Examples of data analysis of the synthetic time series are shown for the ease in which the input signal is represented by a sequence of wavelets     

A robust method for parameter estimation of AR systems using empirical mode decomposition

This paper presents a robust algorithm for parameter estimation of autoregressive (AR) systems in noise using empirical mode decomposition (EMD) method. The basic idea is to represent the autocorrelation function of the noise-free AR signal as the summation of damped sinusoidal functions and use EMD for extracting these component functions as intrinsic mode functions (IMFs). Unlike conventional correlation-based techniques, the proposed scheme first estimates the damped sinusoidal model parameters from the IMFs of autocorrelation function using a least-squares based method. The AR parameters are then directly obtained from the extracted sinusoidal model parameters. Simulation results show that EMD is a very promising tool for AR system identification at a very low signal-to-noise ratio (SNR).     

基于Ozaktas算法的LFM信号参数估计误差分析

Ozaktas算法因其运算复杂度低、精度高、提出时间早而成为目前对LFM信号进行处理时最为常用的离散分数阶Fourier变换算法,但其附加的量纲归一化对LFM信号参数估计存在影响。为此,在对LFM信号参数估计建模基础上,分析了基于Ozaktas算法的参数估计二维离散网格效应,并进一步得到了影响初始频率和调频率估计精度的因素。可以发现:在满足采样定理条件下,基于Ozaktas算法的LFM信号参数估计能保持较好的估计精度,且在一定程度上可以通过增大采样频率或减小采样时长来进一步提高估计精度。最后,通过仿真分析验证了上述理论推导的正确性。     

On-line spectral estimation of nonstationary time series based onAR model parameter estimation and order selection with a forgettingfactor

A new method for on-line spectral estimation of nonstationary time series via autoregressive (AR) model construction is proposed. The method consists of on-line parameter estimation based on the recursive least squares ladder estimation algorithm with a forgetting factor and on-line order determination based on AIC with some modifications. The effectiveness of the proposed method is demonstrated by computer simulation study and applying to the actual data of electroencephalogram (EEG)     

Multitaper spectrum estimation for time series with gaps

Gaps in time series can produce spurious features in power spectrum estimates. These artifacts can be suppressed by averaging spectrum estimates obtained by first windowing the time series with a collection of orthogonal tapers. Such "multitaper" methods have been used for data without gaps since the early 1980s and for more general sampling schemes since the late 1980s. We introduce three families of tapers for time series with gaps. Two of the families solve optimization problems. They minimize bounds on different measures of bias. Computing them involves solving large eigenvalue problems with special structure that can be exploited to construct efficient algorithms. The third family solves no particular optimization problem but is inexpensive to compute and gives spectrum estimates that are quite similar to the other two for actual and simulated helioseismic data. All three families of gap-adapted multitaper estimates have lower variance and bias than the periodogram. In simulations of helioseismic data with gaps, standard methods for constructing confidence intervals for multitaper spectrum estimates, including parametric approximations and resampling in the temporal and spectral domains, all failed to attain their nominal confidence level. We present a novel resampling technique that, in the same simulations, gave confidence intervals that attained the correct confidence level.     

An efficient algorithm for time delay estimation

We present a conceptually simple and computationally efficient algorithm, which is referred to as WRELAX for the well-known time delay estimation problem. The method is a relaxation-based minimizer of a complicated nonlinear least squares criterion, WRELAX can be applied to detecting and classifying roadway subsurface anomalies by using an ultra-wideband ground-penetrating radar. Numerical and experimental examples are provided to demonstrate the performance of the new algorithm     

Robust recursive time series modeling based on an AR model excitedby a t-distribution process

In this correspondence, a new robust recursive spectral estimation based on an AR model is proposed. The optimal coefficients are selected by assuming that the excitation signal has a t-distribution t(α) with α degrees of freedom. With α=∞, we get the RLS method. Simulation results show that the obtained estimates using the proposed method with small α are more efficient, and the standard deviation (SD) of the estimation results is smaller and more accurate than that with large α. The proposed estimator with small α is also more efficient and more accurate than the recursive method based on Huber's M estimate. Two approaches are used, i.e., the infinite memory and the exponentially weighted approaches     

An algorithm for time series analysis of ice sheet surfaceelevations from satellite altimetry

A new method to derive the seasonal elevation signal from a continuous time series of altimeter crossover data is presented. The method utilizes the complete set of intrasatellite crossover data to provide fine temporal resolution and to significantly reduce random measurement errors. This is an improvement over the standard time-series method that only uses those data referenced to a single time period at the beginning of the satellite mission     

A fast parameter estimation algorithm for polyphase coded CW signals

A fast parameter estimation algorithm is discussed for a polyphase coded Continuous Waveform (CW) signal in Additive White Gaussian Noise (AWGN). The proposed estimator is based on the sum of the modulus square of the ambiguity function at the different Doppler shifts. An iterative refinement stage is proposed to avoid the effect of the spurious peaks that arise when the summation length of the estimator exceeds the subcode duration. The theoretical variance of the subcode rate estimate is derived. The Monte-Carlo simulation results show that the proposed estimator is highly accurate and effective at moderate Signal-to-Noise Ratio (SNR).     

Adaptive AR modeling of nonstationary time series by means ofKalman filtering

An adaptive on-line procedure is presented for autoregressive (AR) modeling of nonstationary multivariate time series by means of Kalman filtering. The parameters of the estimated time-varying model can be used to calculate instantaneous measures of linear dependence. The usefulness of the procedures in the analysis of physiological signals is discussed in two examples: first, in the analysis of respiratory movement, heart rate fluctuation, and blood pressure, and second, in the analysis of multichannel electroencephalogram (EEG) signals. It was shown for the first time that in intact animals the transition from a normoxic to a hypoxic state requires tremendous short-term readjustment of the autonomic cardiac-respiratory control. An application with experimental EEG data supported observations that the development of coherences among cell assemblies of the brain is a basic element of associative learning or conditioning     

一种线性调频信号参数估计实时系统的设计

首先简要分析了线性调频信号的特点和参数估计方法,提出了一种基于相关解调、STFT和最大似然估计的参数估计方法。该方法结合了各种方法的优点,在计算量小的情况下得到了比较高的估计精度,通过分析指出使用该方法设计的系统是在现有硬件条件下可实现的实时参数估计系统,并给出了该方法的具体步骤和方针结果。     

Research on intelligent fault diagnosis based on time series analysis algorithm

Aiming to realize fast and accurate fault diagnosis in complex network environment, this article proposes a set of anomaly detection algorithm and intelligent fault diagnosis model. Firstly, a novel anomaly detection algorithm based on time series analysis is put forward to improve the generalized likelihood ratio （GLR） test, and thus, detection accuracy is enhanced and the algorithm complexity is reduced. Secondly, the intelligent fault diagnosis model is established by introducing neural network technology, and thereby, the anomaly information of each node in end-to-end network is integrated and processed in parallel to intelligently diagnose the fault cause. Finally, server backup solution in enterprise information network is taken as the simulation scenario. The results demonstrate that the proposed method can not only detect fault occurrence in time, but can also implement online diagnosis for fault cause, and thus, real-time and intelligent fault management process is achieved.     

一种LFM信号最大似然估计模型与参数估计快速算法

从最大似然估计模型入手,提出了一种适合在一般高斯噪声环境（包括色噪声）下LFM信号目标的参数估计模型和基于此估计模型的调频斜率和初始频率估计的快速算法。此方法获得了最大似然方法估计精度高的优点,且运算量比传统的最大似然方法大大降低。另外推导出了一般高斯环境下的LFM参数估计的CRB界,为一般高斯环境下的估计的参数的方差提供实际下界。     

ADAPTIVE ALGORITHM FOR CONSISTENTMA PARAMETER ESTIMATION VIATHIRD ORDER CUMULANT

This paper addresses the problem of adaptive,consistent parameter estimation for a MA model from the 3rd order cumulant of the system output. The proposed adaptive algorithm is derived by using the new linear equation system (J. K. Tugnait, 1990), which is proved to have unique solution,and hence guarantees the consistence of the MA parameters. Simulation results are provided to show the performance of the new algorithm.     

Blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing

A blind parameter estimation algorithm for frequency-hopping signals based on compressed sensing was proposed,in order to solve the problem that the existing parameter estimation algorithms did not take into account the sparse structural characteristics of the signals in frequency domain.Firstly,the maximum cosine method was used to process the segmented compressed sampling signals,and the hopping frequency was estimated.Then,the atom matching algorithm was used to process the signal with the hopping point,and the frequency hopping instance time was estimated accurately.Then the hopping speed and hopping time were estimated.The experimental results show that the algorithm can significantly reduce the sampling data and computational complexity,while improving estimation accuracy.     

Ocean parameter adaptive time synchronization algorithm for ocean sensor network

An ocean parameter adaptive time synchronization (OPA-Sync) algorithm was proposed,which based on the influence of ocean parameters in different conditions.It improved the adaptation of time synchronization algorithm by adjusting the ocean parameters in the complex and varied environment.And the algorithm was optimized to improve the operation efficiency.Simulation results show that OPA-Sync can be adapted to different ocean environments,which is better than other existing time synchronization algorithms on the synchronization precision and efficiency.