• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.615-618
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• 2004

For successful guided-wave damage inspection, the appropriate signal processing of measured wave signals is very important. The objective of this paper is to introduce an efficient signal processing technique especially suitable for the guided-waves used for damage detection. The key idea of this technique is to model guided-waves by chirp functions of special form considering the dispersion phenomenon. To determine the parameter of the chirp functions simulating guided-waves, the matching pursuit algorithm is employed. The damage information in waveguides can be extracted by pulse-characterizing parameters. The effectiveness of present method is checked with the longitudinal wave-based damage inspection.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.77-85
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• 2001

This paper proposes a method of time-scale modification of polyphonic audio signals based on a sinusoidal model. The signals are modeled with sinusoidal component and noise component. A multiresolution filter bank is designed which splits the input signal into six octave-spaced subbands without aliasing and sinusoidal modeling is applied to each subband signal. To alleviate smearing of transients in time-scale modification a dynamic segmentation method is applied to subbands which determines the analysis-synthesis frame size adaptively to fit time-frequency characteristics of the subband signal. For extracting sinusoidal components and calculating their parameters matching pursuit algorithm is applied to each analysis frame of subband signal. In accordance with spectrum analysis a psychoacoustic model implementing the effect of frequency masking is incorporated with matching pursuit to provide a resonable stop condition of iteration and reduce the number of sinusoids. The noise component obtained by subtracting the synthesized signal with sinusoidal components from the original signal is modeled by line-segment model of short time spectrum envelope. For various polyphonic audio signals the result of simulation shows suggested sinusoidal modeling can synthesize original signal without loss of perceptual quality and do more robust and high quality time-scale modification for large scale factor because of representing transients without any perceptual loss.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1313-1320
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• 2015

In this paper, we propose a user detection technique based on parallel orthogonal matching pursuit (POMP) for uplink multi-user random access networks (RANs) with a number of users and receiver antennas. In general RANs, it is difficult to estimate the number of users simultaneously transmitting packets at the receiver because users with data send the data without grant of BS. In this paper, therefore, we modify the original POMP for the RAN and evaluate its performances through extensive computer simulations. Simulation results show that the proposed POMP can effectively detect activated users more than about 2%~8% compared with the conventional OMP in RANs.

• Journal of Broadcast Engineering
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• v.10 no.2
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• pp.166-173
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• 2005

In this paper, we propose a mobile channel estimation algorithm using matching pursuit algorithm for orthogonal frequency division multiplexing (OFDM) systems. Least square (LS) algorithm, which is used as a conventional channel estimation algorithm for OFDM systems, has error probability of channel estimation affected by effects of noise. By estimating the channel of sparse type, the proposed algorithm reduces effects of noise during time intervals that multi-path signal doesn't exist. The proposed algorithm estimates a mobile receivingchannel using pilot information transmitted consequently. We compare performance of the proposed algorithm with the LS algorithm by measuring symbol error rate with 64QAM under a mobile multi-path fading channel model.

• ETRI Journal
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• v.42 no.6
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• pp.815-826
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• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• Journal of IKEEE
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• v.16 no.3
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• pp.258-264
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• 2012

Although linear channel estimation for the frequency selective fading channel has been widely deployed, its accuracy depends on the number of pilots to probe the channel. Thus, it is unavoidable to employ large number of pilots to enhance the channel estimation performance, which essentially leads to the degradation of the transmission efficiency. It even does not utilize the sparseness of the multipath channel. In this paper a sparse channel estimation scheme based on the matching pursuit algorithm and a pilot assignment method, which minimizes the coherence, are proposed. The simulation results reveal that the proposed algorithm shows superior channel estimation performance with fewer pilots to the LS based ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.583-599
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• 2015

We apply the Bayesian matching pursuit (BMP) algorithm to the estimation of time-frequency selective channels in orthogonal frequency division multiplexing (OFDM) systems. By exploiting prior statistics and sparse characteristics of propagation channels, the Bayesian method provides a more accurate and efficient detection of the channel status information (CSI) than do conventional sparse channel estimation methods that are based on compressive sensing (CS) technologies. Using a reasonable approximation of the system model and a skillfully designed pilot arrangement, the proposed estimation scheme is able to address the Doppler-induced inter-carrier interference (ICI) with a relatively low complexity. Moreover, to further reduce the computational cost of the channel estimation, we make some modifications to the BMP algorithm. The modified algorithm can make good use of the group-sparse structure of doubly selective channels and thus reconstruct the CSI more efficiently than does the original BMP algorithm, which treats the sparse signals in the conventional manner and ignores the specific structure of their sparsity patterns. Numerical results demonstrate that the proposed Bayesian estimation has a good performance over rapidly time-varying channels.

• ETRI Journal
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• v.42 no.3
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• pp.376-387
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• 2020

Massive computation of the reconstruction algorithm for compressive sensing (CS) has been a major concern for its real-time application. In this paper, we propose a novel high-speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Least-squares problem solving, which requires a huge amount of computations in the OMP, is implemented by using systolic arrays with four new processing elements. In addition, a distributed-arithmetic-based circuit for matrix multiplication is proposed to counterbalance the area overhead caused by the multi-stage pipelining. The results of logic synthesis show that the proposed design reconstructs signals nearly 19 times faster while occupying an only 1.06 times larger area than the existing designs for N = 256, M = 64, and m = 16, where N is the number of the original samples, M is the length of the measurement vector, and m is the sparsity level of the signal.

• Journal of IKEEE
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• v.17 no.3
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• pp.339-345
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• 2013

In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.