The author investigates the sensitivity of the RLS algorithm to random perturbations about the optimum filter coefficients. Expressions are derived for the mean and variance of the deviation from the optimum error pow...
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The author investigates the sensitivity of the RLS algorithm to random perturbations about the optimum filter coefficients. Expressions are derived for the mean and variance of the deviation from the optimum error power for the prewindowed growing memory and the experimentally windowed ( lambda >1) RLS algorithm. The results show that the variance of the deviation for correlated signals increases compared to uncorrelated white signals by a term related to the sum of the square of the off-diagonal elements of the sample autocorrelation matrix. For lambda >1, the deviations are bounded and inversely proportional to 1- lambda . For lambda =1, the deviations are unbounded and increase linearly with the number of iterations.
An identification procedure is proposed for a nonGaussian white-noise-driven, linear, time-invariant, nonminimum-phase FIR (finite-impulse response) system. The method is based on parametric modeling of the third mome...
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An identification procedure is proposed for a nonGaussian white-noise-driven, linear, time-invariant, nonminimum-phase FIR (finite-impulse response) system. The method is based on parametric modeling of the third moments of the output sequence and uses causal and anticausal autoregressive moving-average (ARMA) models. The magnitude and phase response of the system are expressed in terms of the AR parameters of the ARMA models. In particular, the AR part of the causal ARMA model captures the minimum-phase component of the system, and the AR part of the anticausal ARMA captures the maximum-phase component. Both sets of parameters are obtained by solving overdetermined linear systems of equations. A model-order-selection criterion based on third-order moments is proposed. The ARMA bispectrum approach is compared to more conventional approaches for magnitude and phase reconstruction. It is demonstrated that the proposed identification procedure exhibits improved modeling performance. The method does not require knowledge of the non-Gaussian noise distribution.< >
A novel class of methods that estimate the difference in arrival time between signals corrupted by spatially correlated Gaussian noise sources of unknown cross correlation is presented. The methods are based on the id...
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A novel class of methods that estimate the difference in arrival time between signals corrupted by spatially correlated Gaussian noise sources of unknown cross correlation is presented. The methods are based on the idea of comparing the similarities between the two sensor measurements in higher-order spectrum domains (bispectrum) rather than in the cross-correlation domain. It is demonstrated that estimation techniques based on higher-order cumulants suppress the effect of correlated Gaussian noise sources and therefore exhibit improved performance over generalized cross-correlation methods. Results are reported for different types of signals, lengths of data records, and signal-to-noise ratios.< >
A new procedure is proposed for ARMA modeling of fourth-order cumulants and trispectrum estimation of non-Gaussian stationary random processes. The new procedure is applied to the identification of nonminimum phase sy...
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A new procedure is proposed for ARMA modeling of fourth-order cumulants and trispectrum estimation of non-Gaussian stationary random processes. The new procedure is applied to the identification of nonminimum phase systems for both phase and magnitude response estimation. It is demonstrated by means of comprehensive simulation examples that the ARMA approach exhibits improved performance over conventional trispectrum methods. ARMA model order selection criteria based on fourth-order cumulants are presented and their performance evaluated. The computational complexity of the ARMA and conventional trispectrum methods is also examined. The new procedure does not require knowledge of the non-Gaussian distribution.
The three-dimensional Schrödinger equation inverse scattering problem with a nonspherically-symmetric potential is related to the filtering problem of computing the linear leastsquares estimate of the three-dimen...
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A computationally efficient identification procedure is proposed for a nonGaussian white-noise-driven linear, time-invariant, nonminimum phase system. The method is based on the idea of computing the complex cepstrum ...
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A computationally efficient identification procedure is proposed for a nonGaussian white-noise-driven linear, time-invariant, nonminimum phase system. The method is based on the idea of computing the complex cepstrum of higher order cumulants of the system output. In particular, the differential cepstrum parameters of the nonminimum phase impulse response are estimated directly from higher-order cumulants by least-squares solution or two-dimensional FFT operations. The method reconstructs the minimum-phase and maximum-phase impulse response components separately. It is flexible enough to be applied on autoregressive (AR), moving average (MA), or ARMA system without a priori knowledge of the type of the system. Benchmark simulation examples demonstrate the effectiveness of the method even with short length data records.< >
Images in real life are inhomogeneous while the transmission channels are time varying, hence, adaptive schemes are suitable for tracking slow variations on system identification and inverse filtering. In this paper w...
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In this paper, we propose an image coding scheme, multistage adaptive vector quantization with least sqaures approximation. In this scheme, an image of size 2n × 2n is first partitioned into 2m × 2m blocks (...
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A multihop packet radio network using receiver-directed code-division multiple access (CDMA) is considered. The network is modeled as a system of interacting queues, and sufficient conditions for the stability of the ...
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A multihop packet radio network using receiver-directed code-division multiple access (CDMA) is considered. The network is modeled as a system of interacting queues, and sufficient conditions for the stability of the queueing system model are obtained. Under an independence assumption, approximate results for the mean packet delay in a symmetric network are given. The results are used to characterize the throughput-delay performance of a symmetric network using frequency-hop spread-spectrum signalling.< >
The general order, multichannel FTF algorithm is a generalization of the multichannel FTF algorithm in which the orders of the input channels can be independently specified. The algorithm is applied to a special case ...
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The general order, multichannel FTF algorithm is a generalization of the multichannel FTF algorithm in which the orders of the input channels can be independently specified. The algorithm is applied to a special case of a two-input-channel, joint process estimator. One of the input channels is defined as the unit-delayed joint process signal; the configuration is an 'equation error' form for least squares (LS) pole-zero ARMA (autoregressive moving-average) system identification. This form is also used in decision-directed equalization and echo cancellation. For this special two-channel case, a computational savings of 2N+1 operations per recursive update is identified, where N is the total number of filter taps. The saving is proven using the concepts of projection onto a vector space. The reduction is available with any of the order-N 'fast' LS algorithms including the fast Kalman (FK) and FAEST algorithms; this had not been recognized for similar application of the FK algorithm to decision-directed equalization. A summary of the modified algorithm is given, with an operations count for each step.< >
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