Prediction Error Filter Research Articles

Adaptive PD+I Control of a Switch-Mode DC–DC Power Converter Using a Recursive FIR Predictor

This paper presents an alternative technique for the adaptive control of power electronic converter circuits. Specific attention is given to the adaptive control of a dc-dc converter. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). The method is computationally efficient and based around a dichotomous coordinate descent (DCD) algorithm. The DCD-recursive least squares (RLS) algorithm has been employed as the adaptive PEF to reduce the computational complexity of existing RLS algorithms for efficient hardware implementation. Results show that the DCD-RLS is able to improve the dynamic performance and convergence rate of the adaptive gains (filter taps) within the controller. In turn, this yields a significant improvement in the overall dynamic performance of the closed-loop control system, particularly in the event of abrupt parameter changes. The proposed controller uses an adaptive proportional-derivative+integral (PD +I) structure which, alongside the DCD algorithm, offers an effective substitute to a conventional proportional-integral-derivative (PID) controller. The nonadaptive integral controller (+I), introduced in the feedback loop, increases the excitation of the filter tap weight and ensures good regulation. The approach results in a fast adaptive controller with self-loop compensation. This is required to minimize the prediction error signal and, in turn, minimize the voltage error signal in the loop by automatically calculating the optimal pole locations. The prediction error signal is further minimized through a second-stage FIR filter (adaptation gain stage). This ensures that the adaptive gains converge to their optimal value. This paper presents detailed simulation analysis and experimental validation on a prototype synchronous dc-dc buck converter. The experimental results clearly demonstrate the superior dynamic performance and voltage regulation compared to conventional PID and adaptive LMS control schemes, with only a modest increase in the computational burden to the microprocessor.

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.

Multidimensional convolution via a 1D convolution algorithm

The helix transform was introduced to the geophysical community by Claerbout (1998) as a means to perform multidimensional convolution via one-dimensional convolution operators. The helix algorithm proves to be very helpful for multidimensional deconvolution problems like those encountered in noise attenuation and seismic data regularization with prediction error filters (Naghizadeh and Sacchi, 2009). The helix transform can be clearly explained using zero-padding and lexicographic ordering of multidimensional data cubes. The intention of this short presentation is to describe, in very simple terms, how one can perform multidimensional convolution via a 1D convolution algorithm.

Adaptive multiple subtraction using a constrained L1-norm method with lateral continuity

The L1-norm method is one of the widely used matching filters for adaptive multiple subtraction. When the primaries and multiples are mixed together, the L1-norm method might damage the primaries, leading to poor lateral continuity. In this paper, we propose a constrained L1-norm method for adaptive multiple subtraction by introducing the lateral continuity constraint for the estimated primaries. We measure the lateral continuity using prediction-error filters (PEF). We illustrate our method with the synthetic Pluto dataset. The results show that the constrained L1-norm method can simultaneously attenuate the multiples and preserve the primaries.

Interferometric interpolation of missing seismic data

We have developed an interferometric interpolation method for filling in trace gaps in common-shot gathers. There are two steps for implementation. First, virtual traces are created in the trace gaps by crosscorrelating recorded traces in a shot gather followed by summing correlated trace pairs over all shot positions. Second, a 1D matching filter is applied to the predicted events to correct for wavelet and amplitude distortions. By using a 1D assumption (assuming a layered medium around the midpoint in a common-midpoint (CMP) gather), the interferometric interpolation method can be applied to CMP gathers. It is a fully data-driven method that requires only the original data. Results with synthetic and field data empirically show that the near-offset traces can be interpolated; but because of the approximations introduced by a limited recording aperture, there are still some artifacts. For comparison, a 2D nonstationary prediction-error filter (PEF) was also used instead of a 1D matching filter in the second step of the interferometric interpolation method. The comparison between the 1D matching filter results and 2D PEF results does not show significant differences.

Adaptive multiple subtraction based on constrained independent component analysis

Adaptive multiple subtraction is a critical and challenging procedure for the widely used surface-related multiple elimination techniques. One of the problems encountered in this procedure is that a good result usually is hard to obtain when primaries and multiples have overlap or when the true and predicted multiples have mismatches, such as wavelet difference, time shift, and scalar inconsistency. We propose an adaptive multiple subtraction method based on constrained independent component analysis (CICA). It combines the advantages of two current adaptive multiple subtraction methods: the independent component analysis method and the multidimensional prediction error filters (PEF) method. In CICA, the prediction error obtained by the PEFs, which measures the lateral continuity of the primaries, is adopted as a constraint term of its objective function.

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.

Frequency extrapolation to enhance the deconvolution of transmitted seismic waves

We investigate the enhanced deconvolution of transmitted seismic waves from distant natural sources using autoregressive extrapolation (AR) and extended time-domain deconvolution. The amplitude spectrum of deconvolved seismograms is often restricted to a reduced frequency range from the use of a water table for the deconvolution. The attenuation effects on the teleseismic seismic waves also reduce the frequency content of the data. We compare the deconvolved spectra obtained from an AR-extended deconvolution (EARD) and an extended time-domain deconvolution (ETDD) technique for teleseismic waves. For EARD, we analyse the spectral content for the deconvolved spectra to differentiate between the domains of known and unknown spectral values. A prediction error filter is used to perform the autoregressive extrapolation to estimate the unknown spectral values. This procedure is applied on 1D and 2D synthetic data to test the approach. The EARD approach is then compared with the ETDD approach which applies an extended high-pass filter to the time-domain deconvolution approach. Both the EARD and ETDD approaches for extending the effective frequency range of the deconvolution results are then compared using observed teleseismic data recorded in southern India.

A Two-Stage Approach for Improving the Convergence of Least-Mean-Square Adaptive Decision-Feedback Equalizers in the Presence of Severe Narrowband Interference

It has previously been shown that a least-mean-square (LMS) decision-feedback filter can mitigate the effect of narrowband interference (L.-M. Li and L. Milstein, 1983). An adaptive implementation of the filter was shown to converge relatively quickly for mild interference. It is shown here, however, that in the case of severe narrowband interference, the LMS decision-feedback equalizer (DFE) requires a very large number of training symbols for convergence, making it unsuitable for some types of communication systems. This paper investigates the introduction of an LMS prediction-error filter (PEF) as a prefilter to the equalizer and demonstrates that it reduces the convergence time of the two-stage system by asmuch as two orders of magnitude. It is also shown that the steady-state bit-error rate (BER) performance of the proposed system is still approximately equal to that attained in steady-state by the LMS DFE-only. Finally, it is shown that the two-stage system can be implemented without the use of training symbols. This two-stage structure lowers the complexity of the overall systemby reducing the number of filter taps that need to be adapted, while incurring a slight loss in the steady-state BER.

Direct multichannel predictive deconvolution

The Levinson principle generally can be used to compute recursively the solution of linear equations. It can also be used to update the error terms directly. This is used to do single-channel deconvolution directly on seismic data without computing or applying a digital filter. Multichannel predictive deconvolution is used for seismic multiple attenuation. In a standard procedure, the prediction-error filter matrices are computed with a Levinson recursive algorithm, using a covariance matrix of the input data. The filtered output is the prediction errors or the nonpredictable part of the data. Starting with the classical Levinson recursion,wehave derived new algorithms for direct recursive calculationof the prediction errors without computing the data covariance-matrix or computing the prediction-error filters. One algorithm generates recursively the one-step forward and backward predic-tion errors and the L-step forward prediction error, computing only the filter matrices with the highest index. A numerically more stable algorithm uses reduced QR decomposition or singular-value decomposition (SVD) in a direct recursive computation of the prediction errors without computing any filter matrix. The new, stable, predictive algorithms require more arithmetic opera-tions in the computer, but the computer programs and data flow are much simpler than for standard predictive deconvolution.

Least-squares attenuation of reverse-time-migration artifacts

Reverse-time-migration artifacts occur when diving waves, head waves or backscattered waves crosscorrelate. These events are particularly strong where high velocity contrasts exist. Simple filtering of the final image can lead to good results but might compromise the integrity of the signal of interest. We demonstrate that a better technique is to apply least-squares filtering with prediction-error filters, a method traditionally used for S/N separation.

Eigen filter to detect volitional EMG signals in autogenic EMG-controlled FES

Presented is an eigen filter to remove autogenic electrical stimulation artefacts and M-waves while preserving the volitional electromyographic signal. The optimality of the eigen filter is shown, along with simulated and measured data to indicate its superior performance over conventional prediction error filters.

Seismoelectric data processing for surface surveys of shallow targets

The utility of the seismoelectric method relies on the development of methods to extract the signal of interest from background and source-generated coherent noise that may be several orders-of-magnitude stronger. We compare data processing approaches to develop a sequence of preprocessing and signal/noise separation and to quantify the noise level from which we can extract signal events. Our preferred sequence begins with the removal of power line harmonic noise and the use of frequency filters to minimize random and source-generated noise. Mapping to the linear Radon domain with an inverse process incorporating a sparseness constraint provides good separation of signal from noise, though it is ineffective on noise that shows the same dip as the signal. Similarly, the seismoelectric signal and noise do not separate cleanly in the Fourier domain, so [Formula: see text]-[Formula: see text] filtering can not remove all of the source-generated noise and it also disrupts signal amplitude patterns. We find that prediction-error filters provide the most effective method to separate signal and noise, while also preserving amplitude information, assuming that adequate pattern models can be determined for the signal and noise. These Radon-domain and prediction-error-filter methods successfully separate signal from [Formula: see text] stronger noise in our test data.

A Nonlinear Prediction Approach to the Blind Separation of Convolutive Mixtures

We propose a method for source separation of convolutive mixture based on nonlinear prediction-error filters. This approach converts the original problem into an instantaneous mixture problem, which can be solved by any of the several existing methods in the literature. We employ fuzzy filters to implement the prediction-error filter, and the ecacy of the proposed method is illustrated by some examples.

Efficient Spatial Image Watermarking via New Perceptual Masking and Blind Detection Schemes

The aim of this paper is to improve the performance of spatial domain watermarking. To this end, a new perceptual mask and a new detection scheme are proposed. The proposed spatial perceptual mask is based on the cover image prediction error sequence and matches very well with the properties of the human visual system. It exhibits superior performance compared to existing spatial masking schemes. Moreover, it allows for a significantly increased strength of the watermark while, at the same time, the watermark visibility is decreased. The new blind detection scheme comprises an efficient prewhitening process and a correlation-based detector. The prewhitening process is based on the least-squares prediction error filter and substantially improves the detector's performance. The correlation-based detector that was selected is shown to be the most suitable for the problem at hand. The improved performance of the proposed detection scheme has been justified theoretically for the case of linear filtering plus noise attack and through extensive simulations. The theoretical analysis is independent of the proposed mask and the derived expressions can be used for any watermarking technique based on spatial masking. It is shown though that in most cases the detector performs better if the proposed mask is employed.

Gram-Schmidt M-Wave Canceller for the EMG Controlled FES

To use the voluntary electromyogram (EMG) as a control signal of the EMG controlled functional electrical stimulator (FES), it is required to reduce the stimulation artifact and non-voluntary contribution (M-wave). In this study, a Gram-Schmidt (GS) prediction error filter (PEF) that can effectively eliminates the M-wave from voluntary EMG is presented. Also, the presented GS PEF is implemented on the field the programmable gate array (FPGA) for real-time processing and the performance is tested with simulated and real signals. Experimental results showed that GS-PEF was effective in reducing M-wave and preserving voluntary EMG.

On merging high- and low-resolution DEMs from TOPSAR and SRTM using a prediction-error filter

High-resolution digital elevation models (DEMs) are often limited in spatial coverage; they also may possess systematic artifacts when compared to comprehensive low-resolution maps. Here we correct artifacts and interpolate regions of missing data in airborne Topographic Synthetic Aperture Radar (TOPSAR) DEMs using a low-resolution Shuttle Radar Topography Mission (SRTM) DEM. We use a prediction error (PE) filter to interpolate and fill missing data so that the interpolated regions have the same spectral content as the valid regions of the TOPSAR DEM. The SRTM DEM is used as an additional constraint in the interpolation. We use cross-validation methods to obtain the optimal weighting for the PE filter and SRTM DEM constraints.

Multiple attenuation in complex geology with a pattern-based approach

Primaries (signal) and multiples (noise) often exhibit different kinematics and amplitudes (i.e., patterns) in time and space. Multidimensional prediction-error filters (PEFs) approximate these patterns to separate noise and signal in a least-squares sense. These filters are time-space variant to handle the nonstationarity of multioffset seismic data. PEFs for the primaries and multiples are estimated from pattern models. In an ideal case where accurate pattern models of both noise and signal exist, the pattern-based method recovers the primaries while preserving their amplitudes. In the more general case, the pattern model of the multiples is obtained by using the data as prediction operators. The pattern model of the primaries is obtained by convolving the noise PEFs with the input data. In this situation, 3D PEFs are preferred to separate (in prestack data) the multiples properly and to preserve the primaries. Comparisons of the proposed method with adaptive subtraction with an [Formula: see text] norm demonstrate that for a given multiple model, the pattern-based approach generally attenuates the multiples and recovers the primaries better. In addition, tests on a 2D line from the Gulf of Mexico demonstrate that the proposed technique copes fairly well with modeling inadequacies present in the multiple prediction.