• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.114-120
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• 2003

Blind channel estimation of communication channels is a problem of important current theoretical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. This paper proposes the blind adaptive channel estimation using multichannel linear prediction method. Computer simulations are presented to compare the proposed algorithm with the existing ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.69-72
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• 2001

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequence, nor does it require a priori channel information. Recently, Tong et al. proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the second order statistics techniques, fur example, subspace method, prediction error method, and so on. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind equalizer length mismatch as well as for its simple adaptive filter implementation. Unfortunately, the previous one-step prediction error method is known to be limited in arbitrary delay. In this paper, we induce the optimal delay, and propose the adaptive blind equalizer with multi-step linear prediction using RLS-type algorithm. Simulation results are presented to demonstrate the proposed algorithm and to compare it with existing algorithms.

• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.618-629
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• 2009

The MPEG-4 Audio Lossless Coding (ALS) standard belongs to the family MPEG-4 audio coding standards. In contrast to lossy codecs such as AAC, which merely strive to preserve the subjective audio quality, lossless coding preserves every single bit of the original audio data. The ALS core codec is based on forward-adaptive linear prediction, which combines remarkable compression with low complexity. Additional features include long-term prediction, multichannel coding, and compression of floating-point audio material. This paper describes the basic elements of the ALS codec with a focus on prediction, entropy coding, and related tools and points out the most important applications of this standardized lossless audio format.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.85-93
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• 2007

In case that multichannel blind deconvolution (MBD) applies to signals of which autocorrelation has a high level, separated signals are temporally whitened by diagonal elements of a separation filter matrix. In order to reduce this distortion, the algorithms, which are based on either constraining diagonal elements of a separation filter matrix or estimating a separation filter matrix by using linear prediction residual signals, are presented. Still, some problems are generated in these methods, when we separate reverberation of signals themselves or interference signals from mixed signals. To solve these problems, this paper proposes the multichannel blind deconvolution method which divides processing procedure into the stage to separate interference signals and the stage to eliminate a reverberation of signals themselves. In simulation results, we confirm that the proposed algorithm can solve the problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.753-762
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• 2002

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor dose it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics in investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparisons with existing algorithms are shown for real measured digital microwave channel.

• Geophysics and Geophysical Exploration
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• v.6 no.4
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• pp.171-179
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• 2003

In land and marine seismic survey, we generally set receivers with equal interval suppose that sampling interval Is too narrow. But the cost of seismic data acquisition and that of data processing are much higher, therefore we should design proper receiver interval. Spatial aliasing can be occurred on seismic data when sampling interval is too coarse. If we Process spatial aliasing data, we can not obtain a good imaging result. Trace interpolation is used to improve the quality of multichannel seismic data processing. In this study, we applied the Spitz algorithm which is widely used in seismic data processing. This algorithm works well regardless of dip information of the complex underground structure. Using prediction filter and original traces with linear event we interpolated in f-x domain. We confirm our algorithm by examining for some synthetic data and marine data. After interpolation, we could find that receiver intervals get more narrow and the number of receiver is increased. We also could see that continuity of traces is more linear than before Applying this interpolation algorithm on seismic data with spatial aliasing, we may obtain a better migration imaging.