共查询到20条相似文献,搜索用时 31 毫秒
1.
FU Jian Tan Hongzhou Huang Yihua 《电子科学学刊(英文版)》2007,24(5):649-654
This paper presents a novel approach to structure determination of linear systems along with the choice of system orders and parameters.AutoRegressive (AR),Moving Average (MA) or AutoRegressive-Moving Average (ARMA) model structure can be extracted blindly from the Third Order Cumulants (TOC) of the system output measurements,where the unknown system is driven by an unobservable stationary independent identically distributed (i.i.d.) non-Gaussian signal.By means of the system order recursion,whether the system has an AR structure or has AR part of an ARMA structure is firstly investigated.MA features in the TOC domain is then applied as a threshold to decide if the system is an MA model or has MA part of an ARMA model.Numerical simulations illustrate the generality of the proposed blind structure identification methodology that may serve as a guideline for blind linear system modeling. 相似文献
2.
This paper develops a novel identification methodology for nonminimum-phase autoregressive moving average (ARMA) models of which the models' orders are not given. It is based on the third-order statistics of the given noisy output observations and assumed input random sequences. The semiblind identification approach is thereby named. By the order-recursive technique, the model orders and parameters can be determined simultaneously by minimizing well-defined cost functions. At each updated order, the AR and MA parameters are estimated without computing the residual time series (RTS), with the result of decreasing the computational complexity and memory consumption. Effects of the AR estimation error on the MA parameters estimation are also reduced. Theoretical statements and simulations results, together with practical application to the train vibration signals' modeling, illustrate that the method provides accurate estimates of unknown linear models, despite the output measurements being corrupted by arbitrary Gaussian noises of unknown pdf 相似文献
3.
Estimation of transient signal in additive noise is very important in radar object detection and recognition. This paper presents a new method for transient signal reconstruction based on bispectrum estimation techniques. The third-order cumulants of the received noisy ultra-wide band echoes are acquired first and an ARMA model is then fitted. The bispectrum of the output signal of the ARMA model will be used to reconstruct the transient signal. Simulation results show that the effect is very good even in lower signal-to-noise (SNR) situation. 相似文献
4.
《IEEE transactions on circuits and systems. I, Regular papers》2008,55(7):1988-2001
5.
According to the features of the echoes of the ultra-wide band radar, this paper analyses the estimating effects of choosing the AR model, MA model and ARMA model based on bispectrum. It shows that ARMA model is much better than others. Simulations verify this result, also demonstrate the estimation precision has been improved. ARMA model parametric bispectrum estimation is very efficient to echoes of ultra-wide band radar. 相似文献
6.
The authors propose a novel model, called a system with multiplicity (SWM), which is a refinement of the model proposed by Sakaguchi and Sakai (1989), to represent arbitrary polynomial bispectra. It is shown that an arbitrary polynomial bispectrum of a 1-D signal can always be realized using an SWM with FIR (finite impulse response) components. An algorithm is then developed for the identification of SWM that will match a given polynomial bispectrum. The authors address the problem of simultaneously matching an arbitrary polynomial bispectrum and a rational power spectrum function using an SWM. It is shown that this can always be accomplished by including another LSI component that is driven by a Gaussian input to the system. Experimental results for matching an estimated bispectrum as well as simultaneously matching a polynomial bispectrum and a power spectrum of some 1-D signals are presented. It is shown that in two dimensions an arbitrary polynomial bispectrum cannot always be uniquely modeled using an SWM with 2-D FIR components having different extents 相似文献
7.
Wei Li Wan-Chi Siu 《Signal Processing, IEEE Transactions on》2000,48(4):1144-1153
In this paper, we address the problem of identifying the parameters of the nonminimum-phase FIR system from the cumulants of noisy output samples. The system is driven by an unobservable, zero-mean, independent and identically distributed (i.i.d) non-Gaussian signal. The measurement noise may be white Gaussian, colored MA, ARMA Gaussian processes, or even real. For this problem, two novel methods are proposed. The methods are designed by using higher order cumulants with the following advantages. (i) Flexibility: method 1 employs two arbitrary adjacent order cumulants of output, whereas method 2 uses three cumulants of output: two cumulants with arbitrary orders and the other one with an order equal to the summation of the two orders minus one. Because of this flexibility, we can select cumulants with appropriate orders to accommodate different applications. (ii) Linearity: both the formulations in method 1 and method 2 are linear with respect to the unknowns, unlike the existing cumulant-based algorithms. The post-processing is thus avoided. Extensive experiments with ARMA Gaussian and three real noises show that the new algorithms, especially algorithm 1, perform the FIR system identification with higher efficiency and better accuracy as compared with the related algorithms in the literature 相似文献
8.
The paper makes an attempt to develop least squares lattice algorithms for the ARMA modeling of a linear, slowly time-varying, multichannel system employing scalar computations only. Using an equivalent scalar, periodic ARMA model and a circular delay operator, the signal set for each channel is defined in terms of circularly delayed input and output vectors corresponding to that channel. The orthogonal projection of each current output vector on the subspace spanned by the corresponding signal set is then computed in a manner that allows independent AR and MA order recursions. The resulting lattice algorithm can be implemented in a parallel architecture employing one processor per channel with the data flowing amongst them in a circular manner. The evaluation of the ARMA parameters from the lattice coefficients follows the usual step-up algorithmic approach but requires, in addition, the circulation of certain variables across the processors since the signal sets become linearly dependent beyond certain stages. The proposed algorithm can also be used to estimate a process from two correlated, multichannel processes adaptively allowing the filter orders for both the processes to be chosen independently of each other. This feature is further exploited for ARMA modeling a given multichannel time series with unknown, white input 相似文献
9.
Hasan M.K. Hossain N.M. Naylor P.A. 《Vision, Image and Signal Processing, IEE Proceedings -》2005,152(5):520-526
A novel method for parameter estimation of minimum-phase autoregressive moving average (ARMA) systems in noise is presented. The ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise-free ARMA signal. The AR parameters are obtained directly from the estimates of the damped sinusoidal model parameters with guaranteed stability. The MA parameters are estimated using a correlation matching technique. Simulation results show that the proposed method can estimate the ARMA parameters with better accuracy as compared to other reported methods, in particular for low SNRs. 相似文献
10.
Parsimonious parametric models for nonstationary random processes are useful in many applications. Here, we consider a nonstationary extension of the classical autoregressive moving-average (ARMA) model that we term the time-frequency autoregressive moving-average (TFARMA) model. This model uses frequency shifts in addition to time shifts (delays) for modeling nonstationary process dynamics. The TFARMA model and its special cases, the TFAR and TFMA models, are shown to be specific types of time-varying ARMA (AR, MA) models. They are attractive because of their parsimony for underspread processes, that is, nonstationary processes with a limited time-frequency correlation structure. We develop computationally efficient order-recursive estimators for the TFARMA, TFAR, and TFMA model parameters which are based on linear time-frequency Yule-Walker equations or on a new time-frequency cepstrum. Simulation results demonstrate that the proposed parameter estimators outperform existing estimators for time-varying ARMA (AR, MA) models with respect to accuracy and/or numerical efficiency. An application to the time-varying spectral analysis of a natural signal is also discussed. 相似文献
11.
12.
The problem of determining the AR order and parameters of a nonminimum phase ARMA model from observations of the system output is considered. The model is driven by a sequence of random variables which is assumed unobservable. A novel identification algorithm based on the second- and third-order cumulants of the output sequences is introduced. It performs order-recursively by minimising a well defined cost function. Strong convergence and consistency of the algorithm are proved and the weight of the cost function is balanced between the second-order and the third-order cumulants of output sequences. The influence of the weight on the estimation accuracy is also evaluated. Theoretical analyses and numerical simulations show that the proposed algorithm is satisfactory for both order and parameter identification of an AR model which is subordinate to a nonminimum phase ARMA model 相似文献
13.
Adnan Al-Smadi 《Circuits, Systems, and Signal Processing》2007,26(5):715-731
A new recursive method for estimating the parameters of autoregressive moving average (ARMA) models is presented in this paper.
The recursive linear identification method is developed using higher-order statistics of the observed output data and is
based on a least-squares solution. Namely, a matrix consisting of third-order statistics (or cumulants) of the observed output
data is constructed so that it almost possesses a full rank structure. The signal is embedded in a Gaussian noise that may
be colored. The system is driven by a zero-mean independent and identically distributed non-Gaussian process. The excitation
signal is unobserved. Simulation results are given to illustrate the performance of the proposed algorithm with respect to
existing well-known methods. 相似文献
14.
A third-order cumulants based adaptive recursive least-squares (CRLS) algorithm for the identification of time-invariant nonminimum phase systems, as well as time-variant nonminimum phase systems, has been successfully developed. As higher order cumulants preserve both the magnitude and the phase information of received signals, they have been considered as powerful signal processing tools for nonminimum phase systems. In this paper, the development of the CRLS algorithm is described and examined. A cost function based on the third-order cumulant and the third-order cross cumulant is defined for the development of the CRLS system identification algorithm. The CRLS algorithm is then applied to different moving average (MA) and autoregressive moving average (ARMA) models. In the case of identifying the parameters of an MA model, a direct application of the CRLS algorithm is adequate. When dealing with an ARMA model, the poles and the zeros are estimated separately. In estimating the zeros of the ARMA model, the construction of a residual time-series sequence for the MA part is required. Simulation results indicate that the CRLS algorithm is capable of identifying nonminimum phase and time-varying systems. In addition, because of the third-order cumulant properties, the CRLS algorithm can suppress Gaussian noise and is capable of providing an unbiased estimate in a noisy environment 相似文献
15.
This paper considers the problem of estimating the moving average (MA) parameters of a two-dimensional autoregressive moving
average (2-D ARMA) model. To solve this problem, a new algorithm that is based on a recursion relating the ARMA parameters
and cepstral coefficients of a 2-D ARMA process is proposed. On the basis of this recursion, a recursive equation is derived
to estimate the MA parameters from the cepstral coefficients and the autoregressive (AR) parameters of a 2-D ARMA process.
The cepstral coefficients are computed benefiting from the 2-D FFT technique. Estimation of the AR parameters is performed
by the 2-D modified Yule–Walker (MYW) equation approach. The development presented here includes the formulation for real-valued
homogeneous quarter-plane (QP) 2-D ARMA random fields, where data are propagated using only the past values. The proposed
algorithm is computationally efficient especially for the higher-order 2-D ARMA models, and has the advantage that it does
not require any matrix inversion for the calculation of the MA parameters. The performance of the new algorithm is illustrated
by some numerical examples, and is compared with another existing 2-D MA parameter estimation procedure, according to three
performance criteria. As a result of these comparisons, it is observed that the MA parameters and the 2-D ARMA power spectra
estimated by using the proposed algorithm are converged to the original ones 相似文献
16.
Estimating parameters of almost cyclostationary non-Gaussian moving average (MA) processes using noisy output-only data is considered. It is shown that second-order cyclic correlations of the output are generally insufficient in uniquely characterizing almost periodically time-varying MA(q) models, while third-order and higher order cumulants can be used to estimate their model parameters within a scale factor. Both linear and nonlinear identification algorithms for fixed and time-varying order q(t) are presented. Statistical model order determination procedures are also derived. Implementation issues are discussed and resistance to noise is claimed when the signal of interest has cycles distinct from the additive noise. Simulations are performed to verify the theoretical results 相似文献
17.
非高斯有色噪声中的正弦信号频率估计 总被引:10,自引:1,他引:9
本文研究非高斯ARMA有色噪声中的正弦信号频率估计问题。利用自相关函数和三阶累积量相结合,提出了一种先估计噪声模型AR参数,然后对观测值进行预滤波,最后估计信号模型参数的新方法,模拟实验结果表明,新方法具有良好的频率估计性能。 相似文献
18.
MA parameter estimation and cumulant enhancement 总被引:1,自引:0,他引:1
This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods are also developed and used to claim the uniqueness of the batch least squares solutions. A novel technique for the enhancement of third-order cumulants of MA processes is introduced. This new technique is based on the concept of composite property mappings and helps reduce the variance of the estimates of third- (or fourth)-order cumulants of MA processes. Simulation results are presented that demonstrate the performance of the new methods and compare them with a range of existing techniques 相似文献
19.
20.
This paper addresses the harmonic retrieval problem in non-Gaussian ARMA noise. A hybrid ESPRIT approach using second-and third-order statistics is proposed. First, third-order statistics are used to identify the AR part of the non-Gaussian noise process, then the noisy measurements are filtered by AR polynomial, finally, the harmonic signal parameters are estimated. Simulation examples show the effectiveness and high resolution of the new approach. 相似文献