Linear Prediction Error Filter Research Articles

Online Blind Equalization for QAM Signals Based on Prediction Principle via Complex Echo State Network

Given the nonlinear channel, the online blind equalization algorithm for Quadrature Amplitude Modulation (QAM) signals based on the prediction principle via the Complex Echo State Network(C-ESN) is proposed. The traditional linear prediction error filter is replaced by the C-ESN with good nonlinear mapping ability, and the minimum prediction error is attained by the Kalman filter(KF). After then, the amplitude and phase are adjusted in sequence. Simulation results show that the proposed algorithm can effectively reduce the distortion of the transmitted QAM signals caused by nonlinear channels. Compared with other prediction principle-based blind equalization algorithms and the traditional cost function based ESN blind equalization algorithm, it has lower mean square error and faster convergence speed.

MIMO beamforming system for speech enhancement in realistic environment with multiple noise sources

Multiple noise sources in a realistic environment severely degrade the quality and intelligibility of the desired speech signal, thus posing a severe problem for many speech applications. Several noise reduction algorithms have been proposed with a main goal to solve this problem. However, the good performances of such algorithms are severely impaired in realistic environment under multi-noise sources condition. In this paper, the author treats the noise cancellation system as a multiple-input multiple-output (MIMO) beamformer system. The proposed approach consists of two steps. First, the noise signals are generated by applying the white noise sources to a MIMO AR system. Then, the noisy microphone signals are sequentially processed by employing multi-channel linear prediction error filters (MCLPEFs) and multi-channel adaptive noise estimation filters (MCANEFs) in the lower path of the proposed beamformer. The MCLPEFs are used to whiten the input signals, while the MCANEFs are used as a MIMO system identification to perform the modeling process of the noise signals. Finally, the noise signals in the upper path are subtracted from the estimated noises in the lower path to recover an enhanced speech signal. Moreover, the performance of the proposed MIMO approach was validated under a realistic environment with real noise sources.

Method for reducing of noise by improving Signal-to-Noise-Ratio in wireless LAN

The signal to noise ratio (SNR) is one of the important measures for reducing the noise.A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech and image degraded by additive background noise is proposed. Since a speech signal can be represented as the stationary signal over a short interval of time, most of speech signal can be predicted by the LPEF. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the reconstructed noise from the speech degraded by additive background noise. Most of the MR image accelerating methods suffers from degradation of acquired images, which is often correlated with the degree of acceleration. However, Wideband MRI is a novel technique that transcends such flaws.In this paper we proposed LPEF and ADF for reducing the noise in speech and also we demonstrate that Wideband MRI is capable of obtaining images with identical quality as conventional MR images in terms of SNR in wireless LAN.

Linear prediction based semiblind channel estimation for multiuser OFDM with insufficient guard interval

To meet the demand of high data rate transmissions for multimedia wireless communications, orthogonal frequency division multiplexing (OFDM) systems in conjunction with multiple-input multiple-output (MIMO) signal processing have been considered one of the central techniques in advanced wireless communications. In the paper, two semiblind channel estimation algorithms are proposed for the uplink multiuser OFDM systems with insufficient guard interval, in contrast to sufficient guard interval assumed in most of the prior works. A zero-padding OFDM system, which zero-pads rather than cyclicly prefixing each block, is considered in this paper. By utilizing the relation between the linear prediction error filters (LPEFs) of the received signal with multiple prediction orders and the transmitted data sequence, the first proposed algorithm, namely the multistage LP (MLP) based algorithm, can estimate the MIMO channel coefficients, with only a single pilot OFDM block used. To reduce the sensitivity of the proposed algorithms to the channel order overestimation, it is proposed to implement the LPEFs with a QR-decomposition based approach. This QRdecomposition based approach alternatively computes the LPEFs without direct inversion of the received signal correlation matrix, thus exhibiting robustness against channel order overestimation. Some simulation results are presented to demonstrate the effectiveness and robustness of the proposed algorithms.

Detection of microcalcifications in mammograms using error of prediction and statistical measures

A two-stage method for detecting microcalcifications in mammograms is presented. In the first stage, the determination of the candidates for microcalcifications is performed. For this purpose, a 2-D linear prediction error filter is applied, and for those pixels where the prediction error is larger than a threshold, a statistical measure is calculated to determine whether they are candidates for microcalcifications or not. In the second stage, a feature vector is derived for each candidate, and after a classification step using a support vector machine, the final detection is performed. The algorithm is tested with 40 mammographic images, from Screen Test: The Alberta Program for the Early Detection of Breast Cancer with 50-µm resolution, and the results are evaluated using a free-response receiver operating characteristics curve. Two different analyses are performed: an individual microcalcification detection analysis and a cluster analysis. In the analysis of individual microcalcifications, detection sensitivity values of 0.75 and 0.81 are obtained at 2.6 and 6.2 false positives per image, on the average, respectively. The best performance is characterized by a sensitivity of 0.89, a specificity of 0.99, and a positive predictive value of 0.79. In cluster analysis, a sensitivity value of 0.97 is obtained at 1.77 false positives per image, and a value of 0.90 is achieved at 0.94 false positive per image.

Robust Detection Algorithm for Spread Spectrum Audio Watermarking

In this letter we propose a robust detection algorithm for audio watermarking for copyright protection. The watermark is embedded in the time domain of an audio signal by the normally used spread spectrum technique. The scheme of detection is an improvement of the conventional correlation detector. A high-pass filter is applied along with the linear prediction error filter for whitening the audio signal and an adaptive threshold is chosen for decision comparing. Experimental results show that our detection algorithm outperforms the conventional one not only because it improves the robustness to normal attacks but also because it can provide the robustness to time-invariant pitch-scale modification.

Speech separation based on law of causality

This paper proposes a microphone array system separating speech signals, based on a different principle from independent component analysis (ICA). This system applies linear prediction error filters to microphone outputs, and using the prediction errors, adjusts the coefficients of adaptive filters. In this case, only the prediction errors satisfying the low of causality become available for the adjustment; consequently, this system can steer a null toward a direction satisfying it. The permutation problem discussed in ICA can be thereby avoided. This system also can compensate the separated speech signals by using adaptive filters, and finally, can provide high quality speech signals. This paper moreover verifies the performance of the proposed system by using speech signal data measured in an ordinary room. This result shows that the proposed system works well even in reverberation environment.

Two-dimensional Fourier series-based model for nonminimum-phase linear shift-invariant systems and texture image classification

In this paper, Chi's (1997, 1999) real one-dimensional (1-D) parametric nonminimum-phase Fourier series-based model (FSBM) is extended to two-dimensional (2-D) FSBM for a 2-D nonminimum-phase linear shift-invariant system by using finite 2-D Fourier series approximations to its amplitude response and phase response, respectively. The proposed 2-D FSBM is guaranteed stable, and its complex cepstrum can be obtained from its amplitude and phase parameters through a closed-form formula without involving complicated 2-D phase unwrapping and polynomial rooting. A consistent estimator is proposed for the amplitude estimation of the 2-D FSBM using a 2-D half plane causal minimum-phase linear prediction error filter (modeled by a 2-D minimum-phase FSBM), and then, two consistent estimators are proposed for the phase estimation of the 2-D FSBM using the Chien et al. (1997) 2-D phase equalizer (modeled by a 2-D all-pass FSBM). The estimated 2-D FSBM can be applied to modeling of 2-D non-Gaussian random signals and 2-D signal classification using complex cepstra. Some simulation results are presented to support the efficacy of the three proposed estimators. Furthermore, classification of texture images (2-D non-Gaussian signals) using the estimated FSBM, second-, and higher order statistics is presented together with some experimental results. Finally, we draw some conclusions.

Autoregressive Model-Based Gear Fault Diagnosis

Abstract This paper presents a model-based technique for the detection and diagnosis of gear faults. Based on the signal averaging technique, the proposed technique first establishes an autoregressive (AR) model on the vibration signal of the gear of interest in its healthy-state. The model is then used as a linear prediction error filter to process the future-state signal from the same gear. The health condition of the gear is diagnosed by characterizing the error signal between the filtered and unfiltered signals. The technique is validated using both numerical simulation and experimental data. The results show that the AR model technique is an effective tool in the detection and diagnosis of gear faults and it may lead to an effective solution for in-flight diagnosis of helicopter transmissions.

Integrated echo and noise canceler for hands-free applications

An integrated acoustic echo and noise canceler is proposed for hands-free telephony applications. The proposed system includes a computationally efficient double-talk detection (DTD) method and a residual echo cancellation algorithm. The DTD method uses two cross-correlation coefficients and is robust in adverse conditions. In the residual echo cancellation approach, a linear prediction error filter removes the short-term correlation of the residual echo and the resulting whitened residual echo is canceled by a noise-reduction filter. The proposed system was implemented using a general purpose digital signal processor (DSP) with 16-b arithmetic.

Two-dimensional frequency-domain blind system identification using higher order statistics with application to texture synthesis

In this paper, Shalvi and Weinstein's (1993) super-exponential (SE) algorithm using higher order statistics for blind deconvolution of one-dimensional (1-D) linear time-invariant systems is extended to a two-dimensional (2-D) SE algorithm. Then, a 2-D frequency-domain blind system identification (BSI) algorithm for 2-D linear shift-invariant (LSI) systems using the computationally efficient 2-D SE algorithm and the 2-D linear prediction error filter is proposed. In addition to the LSI system estimate, the proposed BSI algorithm also provides a minimum mean square error (MMSE) equalizer estimate and an MMSE signal enhancement filter estimate. Then, a texture synthesis method (TSM) using the proposed BSI algorithm is presented. Some simulation results to support the efficacy of the proposed BSI algorithm and some experimental results to support the efficacy of the proposed TSM are presented. Finally, some conclusions are drawn.

Performance bounds for multistep prediction-based blind equalization

Blind equalization attempts to remove the interference caused by a communication channel without using any known training sequences. Blind equalizers may be implemented with linear prediction-error filters (PEFs). For many practical channel types, a suitable delay at the output of the equalizer allows for achieving a small estimation error. The delay cannot be controlled with one-step predictors. Consequently, multistep PEF-based algorithms have been suggested as a solution to the problem. The derivation of the existing algorithms is based on the assumption of a noiseless channel, which results in zero-forcing equalization. We consider the effects of additive noise at the output of the multistep PEF. Analytical error bounds for two PEF-based blind equalizers in the presence of noise are derived. The obtained results are verified with simulations. The effect of energy concentration in the channel impulse response on the error bound is also addressed.

Exponential asymptotic stability of time-varying inverse prediction error filters

It is a classical result of linear prediction theory that as long as the minimum prediction error variance is nonzero, the transfer function of the optimum linear prediction error filter for a stationary process is minimum phase, and therefore, its inverse is exponentially stable. Here, extensions of this result to the case of nonstationary processes are investigated. In that context, the filter becomes time-varying, and the concept of "transfer function" ceases to make sense. Nevertheless, we prove that under mild condition on the input process, the inverse system remains exponentially stable. We also consider filters obtained in a deterministic framework and show that if the time-varying coefficients of the predictor are computed by means of the recursive weighted least squares algorithm, then its inverse remains exponentially stable under a similar set of conditions.

Fourier series based nonminimum phase model for statistical signal processing

In this paper, a parametric Fourier series based model (FSBM) for or as an approximation to an arbitrary nonminimum-phase linear time-invariant (LTI) system is proposed for statistical signal processing applications where a model for LTI systems is needed. Based on the FSBM, a (minimum-phase) linear prediction error (LPE) filter for amplitude estimation of the unknown LTI system together with the Cramer-Rao (CR) bounds is presented. Then, an iterative algorithm for obtaining the optimum LPE filter with finite data is presented that is also an approximate maximum-likelihood algorithm when data are Gaussian. Then three iterative algorithms using higher order statistics (HOS) with finite non-Gaussian data are presented to estimate parameters of the FSBM followed by some simulation results as well as some experimental results with real speech data to support the efficacy of the proposed algorithms using the FSBM. Finally, we draw some conclusions.

A new algorithm for the design of linear prediction error filters using cumulant-based MSE criteria

Proposes a new algorithm for the design of (minimum-phase) linear prediction error (LPE) filters using two new cumulant (higher order statistics) based MSE criteria when the given stationary random signal x(n) is nonGaussian and contaminated by Gaussian noise. It is shown that the designed LPE filters based on the proposed criteria are identical to the conventional correlation (second-order statistics) based LPE filter as if x(n) were noise-free measurements. As correlation-based LPE filters, coefficients of the designed cumulant-based LPE filters can be obtained by solving a set of symmetric Toeplitz linear equations using the well-known computationally efficient Levinson-Durbin recursion. Moreover, the proposed two criteria are applicable for any cumulant order M/spl ges/3, and one of the proposed criteria for M=3 reduces to Delopoulos and Giannakis' (1992) third-order cumulant-based MSE criterion. Some simulation results are then provided to support the analytical results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Linear prediction, maximum flatness, maximum entropy, and AR polyspectral estimation

The authors present a theoretical foundation for polyspectral estimation and modeling of non-Gaussian autoregressive (AR) processes which includes a new higher-order-statistics (HOS)-based linear prediction error filter and associated linear prediction polyspectral estimator, and a maximum higher order entropy polyspectral estimator, and considers the equivalences among these polyspectral estimators. >

EFFECT OF NON GAUSSIAN WEATHER CLUTTER ON PERFORMANCE OF CLUTTER-SUPPRESSION FILTERS

In this paper it is investigated the performance of clutter suppression filters in non Gaussian clutter. The model chosen is Log-normal clutter and the filters considered are the optimum MTI filter and the linear prediction error filter. The performance of both types of filters in Gaussian clutter as expressed by the improvement factor (I.F) is computed for different number of pulses N and different values of correlation coefficient . The results show that the optimal improvement factor is degraded for input non Gaussian clutter. While this situation can be improved if the complex weights are adjusted in correspondence with the covariance matrix of the Log-normal clutter. Comparison of performance of the two types ,of filters for different values of parameters is illust-rated quantitavely in graphical form.

On the zeros of the linear prediction-error filter for deterministic signals

The zeros of a linear prediction-error filter polynomial for a class of deterministic signals, that are a sum of samples of M exponentially damped/undamped sinusoids, is studied. It is assumed that N samples are available for processing and that they are uncorrupted by noise. It is shown that the exponent parameters of the M signals can be determined from M zeros (called "signal zeros") of an Lth degree prediction-error filter polynomial (L>M) if L lies in between M and N - M (N - M/2 in a special case). The rest of the L - M zeros of the filter polynomial switch are called extraneous zeros, are shown to be approximately uniformly distributed with in the unit circle, regardless of the type of exponentials in the signal, if the prediction filter coefficients are chosen to have minimum Euclidean length. The results obtained provide insight into the estimation problem with noisy data.

Linear prediction error filter design

The design of a linear prediction error filter can be formulated as a four‐step procedure. These steps, in part, consist of a variational calculus approach that leads directly to the normal equations and an entropy conservation principle. From the normal equations, we obtain parameters called partial correlation coefficients that minimize the prediction error at the kth filter design step. Partial correlation coefficients determine the best prediction error filter for different seismic trace models (autoregressive, autoregressive‐moving average, and phase‐intercept‐shifted autoregressive traces). A result shows that simple phase distortion can increase the length of the prediction error filter and affect the estimates of the reflection sequence.

Two-dimensional linear prediction: Autocorrelation arrays, minimum-phase prediction error filters, and reflection coefficient arrays

In this paper, a number of results in one-dimensional (1-D) linear prediction theory are extended to the two-dimensional (2-D) case. It is shown that the class of 2-D minimum mean-square linear prediction error filters with continuous support have the minimum-phase property and the correlation-matching property, and that they can be solved by means of a 2-D Levinson algorithm. A significant practical result to emerge from this theory is a reflection coefficient representation for 2-D minimum-phase filters. This representation provides a domain in which to construct 2-D filters, such that the minimum-phase condition is automatically satisfied.