Adaptive Prediction Error Filter Research Articles

Nonstationary pattern-based signal–noise separation using adaptive prediction-error filter

Abstract Complex field conditions always create different interferences during seismic data acquisition, and there exist several types of noise in the recorded data, which affect the subsequent data processing and interpretation. To separate an effective signal from the noisy data, we adopted a pattern-based method with a two-step strategy, which involves two adaptive prediction-error filters (APEFs) corresponding to a nonstationary data pattern and noise pattern. By introducing shaping regularization, we first constructed a least-squares problem to estimate the filter coefficients of the APEF. Then, we solved another constrained least-square problem corresponding to the pattern-based signal–noise separation, and different pattern operators are adopted to characterize random noise and ground-roll noise. In comparison with traditional denoising methods, such as FXDECON, curvelet transform and local time–frequency (LTF) decomposition, we examined the ability of the proposed method by removing seismic random noise and ground-roll noise in several examples. Synthetic models and field data demonstrate the validity of the strategy for separating nonstationary signal and noise with different patterns.

Multichannel adaptive deconvolution based on streaming prediction-error filter

Abstract Deconvolution mainly improves the resolution of seismic data by compressing seismic wavelets, which is of great significance in high-resolution processing of seismic data. Prediction-error filtering/least-square inverse filtering is widely used in seismic deconvolution and usually assumes that seismic data is stationary. Affected by factors such as earth filtering, actual seismic wavelets are time- and space-varying. Adaptive prediction-error filters are designed to effectively characterise the nonstationarity of seismic data by using iterative methods, however, it leads to problems such as slow calculation speed and high memory cost when dealing with large-scale data. We have proposed an adaptive deconvolution method based on a streaming prediction-error filter. Instead of using slow iterations, mathematical underdetermined problems with the new local smoothness constraints are analytically solved to predict time-varying seismic wavelets. To avoid the discontinuity of deconvolution results along the space axis, both time and space constraints are used to implement multichannel adaptive deconvolution. Meanwhile, we define the parameter of the time-varying prediction step that keeps the relative amplitude relationship among different reflections. The new deconvolution improves the resolution along the time direction while reducing the computational costs by a streaming computation, which is suitable for handling nonstationary large-scale data. Synthetic model and field data tests show that the proposed method can effectively improve the resolution of nonstationary seismic data, while maintaining the lateral continuity of seismic events. Furthermore, the relative amplitude relationship of different reflections is reasonably preserved.

Real-Time Artifact Removal System for Surface EMG Processing During Ten-Fold Frequency Electrical Stimulation

In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.

Multidimensional Seismic Data Reconstruction Using Frequency-Domain Adaptive Prediction-Error Filter

Seismic data interpolation and reconstruction play an important role in seismic data processing. Seismic data are often inadequately sampled along various spatial axes. We have developed a new approach to interpolate aliased multidimensional seismic data based on the multidimensional adaptive prediction-error filter in frequency domain. First, we estimate the adaptive prediction-error filter coefficients, then interpolate missing traces using the estimated coefficients. Shaping regularization is used to control the smoothness of frequency-domain multidimensional adaptive prediction-error filter coefficients. Instead of estimating prediction-error filter coefficients only along one direction space, we estimate the prediction-error filter coefficients using more information along different direction spaces. So, multidimensional adaptive prediction-error filter using regularized nonstationary autoregression can adaptively estimate seismic events whose slopes vary in multidimensional space. The frequency-domain multidimensional interpolation method can input data at temporal frequency, which can save computer memory and time. For multidimensional seismic data, which miss different number of traces regularly in different axes, the proposed method can be used to interpolate missing traces to obtain more accurate results. The proposed method improves the calculation efficiency by applying shaping regularization and implementation in the frequency domain. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples.

Widely Linear Adaptive Equalization of Digital Communication Channel

In this paper, we employ the widely linear adaptive processing technique in digital communication channel equalization. We present two new techniques for supervised and unsupervised equalization. In the supervised technique the multi-split transform is aggregated to the widely linear processing, using a power normalized and time-varying step-size LMS algorithm. The unsupervised technique consists of a widely linear adaptive prediction-error filter using the time-varying step-size LMS algorithm proposed by Kwong and Johnston. We show that this technique can invert non-minimum phase channels. The main advantages of the proposed equalizers are the low computational complexity and better performance in terms of convergence rate and accuracy, when compared to the conventional equalization techniques. Simulation results show that the widely linear equalizers have a good performance.

Architecture of a configurable 2-D adaptive filter used for small object detection and digital image processing

A configurable 2-D adaptive digital filter architecture based on 2-D normalized least mean square (TDNLMS) algorithms is proposed. This architecture can be used as a prewhitening filter to enhance the detectability of small objects in digital images. Every tap in this architecture can be configured as one of three types: input, reference, and disable taps. By setting the type of each tap, this architecture can be utilized to implement a TDNLMS adaptive prediction error filter in any type of support region on a 5x5 tap array. This feature allows the adaptive prediction filter to detect small objects whose area changes gradually during the detection process, or to process images with different stochastic characteristics. This architecture can also be configured as an adaptive prediction filter (APF) used in image noise reduction, or a 2-D convolver used in many image processing algorithms. In the calculation of coefficient updating, the power of the input vector is approximate to the power of two, hence the critical path delay is reduced significantly. A local parallel global serial mode is adopted in this architecture to increase the coefficients' word length, and to reduce the finite-precision effect. Simulation results show that TDNLMS APEF based on this architecture can provide satisfactory small object detection performance in various environments.

A secondary path modeling technique for active noise control systems

A secondary path modeling technique for active noise control systems is developed for both on-line and off-line modeling with faster convergence and higher modeling accuracy. The optimum delay for the adaptive prediction error filter to reduce the interference in system modeling is equal to the length of the impulse response of the secondary path being modeled.

Rejection of an FH signal in a DS spread-spectrum system using complex adaptive filters

The performance of a direct sequence QPSK spread-spectrum receiver using adaptive filters in the presence of frequency hopped interference is analyzed. The analysis includes both the adaptive prediction error filters and the adaptive transversal filters with two-sided taps. If the product of the instantaneous frequency offset /spl Omega//sub l/, between the jamming signal and the carrier of the spread-spectrum signal, and the sampling period /spl Delta/ is 360/spl deg/ (/spl Omega//sub l//spl middot//spl Delta/=360/spl deg/), the filter gain is reduced to zero. The filter gain G highly depends on the filter adaptation rate /spl mu/. Depending on /spl mu/, G can vary from zero to more than 20 dB for a jammer/signal power ratio (J/S) of 20 dB. If /spl Omega//sub l//spl middot//spl Delta/ is small enough (/spl les/10/spl deg/), the performance of the transversal filter is better than that of the prediction error filter, in the case when /spl mu/ is small. For larger values of /spl mu/ or /spl Omega//sub l//spl middot//spl Delta/, these performances are approximately the same. Numerical results for the hopping sequence of the jamming signal are also presented. Besides the filter gain the analysis of the adaptation rate (time constant) filter misadjustment and the system bit error probability is also included. >

Eigenvalue behavior in FH signal rejection in DS spread spectrum systems using complex adaptive filters

Eigenvalue behavior of direct sequence (DS) QPSK spread spectrum (SS) receiver using adaptive filters in presence of frequency hopped (FH) interference is analyzed. The analysis is valid both for the adaptive prediction error filters and for transversal filters with two-sided taps. The improvement factor G depends on filter adaptation rate μ and on transient response of the filter. Thansient responses of the filters using eigenvalue behavior method are analyzed. Presented analysis and results show that slow FH jammer suppression in DS system can be obtained using complex adaptive filters. The adaptation rate dependance on different communication parameters and on discrete time is presented.

Rejection of Pulsed CW Interference in PN Spread-Spectrum Systems Using Complex Adaptive Filters

In this paper, the performance of direct sequence QPSK spread-spectrum systems using complex adaptive filters in the presence of pulsed CW interference is analyzed. Both adaptive prediction error filters and adaptive transversal filters with two-sided taps are considered. It is shown that the time constant of the tap weight adaptation in the interference off-interval is usually much greater than the time constant in the on-interval, and that this is beneficial for the system since it results in retaining the rejection property of the filter. Under steady-state conditions, the tap weights are calculated. Analytical expressions for the signal-to-noise ratio improvement under the least favorable interference condition are given. It is shown that the performance of the two-sided transversal filter is better than that of the prediction error filter.