• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.33-42
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. MRFs have been used to model spatially correlated and signal-dependent phenomena for SAR speckled images. The MRF is incorporated into digital image analysis by viewing pixel types as slates of molecules in a lattice-like physical system defined on a GRF Because of the MRF-SRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed Point-Jacobian Iterative MAP estimation method was first evaluated using simulation data generated by the Monte Carlo method. The methodology was then applied to data acquired by the ESA's ERS satellite on Nonsan area of Korean Peninsula. In the extensive experiments of this study, The proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.84-90
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• 2009

In this paper, the new channel estimation was proposed that have the low complexity and high performance using Fuzzy inference method uses recently from various field for estimation about uncertainty in channel estimation of OFDM. Proposed method is channel estimation performance improve, calculation and interpolation for statistics character of channel using the pilot before LS channel estimation by Fuzzy inference method. Simulation result in QPSK proposed channel estimation method shows the enhancement of 5.5dB compared to the LS channel estimation and the deterioration of 1.3dB compared to the MMSE channel estimation in mean square error point $10^{-3}$. symbol error rate shows similarity performance the MMSE $10^{-1.96}$, proposed channel estimation $10^{-1.93}$ and enhancement of $10^{-0.35}$ compared to the LS channel estimation in signal to noise ratio point 20dB.

• Journal of Advanced Navigation Technology
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• v.2 no.2
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• pp.75-83
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• 1998

This paper presents a low cost and high accuracy integrated Global Positioning System (GPS)/dead reckoning (DR) system. The integrated GPS/DR system is capable of providing highly accurate position data in real-time or in post processing. Based on the analysis of the main error source affecting the DR measurements, an eight-state mathematical model for the integrated system has been developed to represent these errors. This eight-state model has been used to build a nonlinear filter for the estimation of the state vector at every epoch when DR measurements are available. The accuracy of the system has been evaluated using 1Hz DR measurements and 3Hz continuous GPS position estimates. Through numerical simulation the system performance during periods with GPS outage has been investigated by comparing two different noise models. While one model is the position estimation filter containing a single noise model, the other filter includes two-level noise model. The simulation results have shown that the estimation filter containing two-level noise model for computing the position error of the integrated GPS/DR system yields better performance than that the filter including the single-level noise model does.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.232-238
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• 2013

This paper study the position estimation of MBK system according to the non-linear filter for non-Gaussian noise in underwater sensor networks. In the filter to estimate location, recently, the extended Kalman filter (EKF) and particle filter are getting attention. EKF is widely used due to the best algorithm in the Gaussian noise environment, but has many restrictions on the usage in non-Gaussian noise environment such as in underwater. In this paper, we propose the improved One-Dimension Particle Filter (ODPF) using the distribution re-interpretation techniques based on the maximum likelihood. Through the simulation, we compared and analyzed the proposed particle filter with the EKF in non-Gaussian underwater sensor networks. In the case of both the sufficient statistical sample and the sufficient calculation capacity, we confirm that the ODPF's result shows more accurate localization than EKF's result.

• Proceedings of the KSR Conference
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• 2002.10b
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• pp.948-953
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• 2002

As the delay of the Kyungbu High Speed Railroad, HST should use conventional line through Daegoo to Pusan until the new railroad build. High speed railroad noise is one of the main causes of environmental impact. Whenever HST on conventional railroad line is planned or a housing project near an existing railroad is proposed, an estimate of the relevant noise levels is usually required. For this, it is necessary to quantify those parameters that affect the railroad noise. This paper deals with an estimation of high speed railroad noise on conventional line.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.222-228
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• 2010

In this paper, we propose a new approach to noise estimation incorporating spectral deviation with soft decision scheme to enhance the intelligibility of the degraded speech signal in non-stationary noisy environments. Since the conventional noise estimation technique based on soft decision scheme estimates and updates the noise power spectrum using a fixed smoothing parameter which was assumed in stationary noisy environments, it is difficult to obtain the robust estimates of noise power spectrum in non-stationary noisy environments that spectral characteristics of noise signal such as restaurant constantly change. In this paper, once we first classify the stationary noise and non-stationary noise environments based on the analysis of spectral deviation of noise signal, we adaptively estimate and update the noise power spectrum according to the classified noise types. The performances of the proposed algorithm are evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various ambient noise environments and show better performances compared with the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.59-63
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• 2011

Conventional noise reduction filtering schemes realize limited improvements of the peak signal-to-noise ratio (PSNR) in the low-level noisy images. The flatness degree and the edge information are effectively used to estimate the noise volume. We propose a noise estimator for reducing noise in the AWGN (additive white gaussian noise) corrupted images using three intermediate image maps (FGM(flatness gray map), FIM(flatness index map), NEM(noise estimate map)). The proposed noise estimator is fed into the conventional noise reduction filters as a pre-processor. The performance of noise reduction is tested in the various AWGN corrupted images.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.96-104
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• 1997

In this paper, we proposed new methods for estimation of time delay and time-frequency delay in impulsive noise environment. The proposed methods are developed using the theory of ${\alpha}-stable$ distribution, including the fractional negative order moment(FNOM) and minimum dispersion(MD), which are formulated for the time delay estimation and the fractional negative order ambiguity function and complex minimum dispersion, which are difined for the joint estimation of time delay and frequency delay. Through simulation work, its performance was compared with various other algorithms. As a result, while the conventional approaches based on second-order statistics are only verified in Gaussian noise environent ($S{\alpha}S$ noise with ${\alpha}$=2) and also the recently proposed robust methods by Nikias[7] are verified only in limited impulse noise ($S{\alpha}S$ noise with the range of $1<{\alpha}{\le}2$), the methods proposed are able to estimate the time delay in Gaussian and any impulsive noise environments($S{\alpha}S$ noise with the range of $0<{\alpha}{\le}2$).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.1
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• pp.30-37
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• 2011

In this paper, we propose a fast ICI suppression algorithm with adaptive channel estimation for the LTE-Advanced uplink system. In order to effectively remove phase noise and carrier frequency offset at time varying channel, we use the comb type pilot. The purpose is to improve performance by reducing computational complexity than conventional PNFS(Phase Noise & Frequency offset Suppression) algorithm. We reduce computational complexity by decreasing overlapping computation or unnecessary computation at conventional PNFS algorithm. Also, we propose an effective channel estimation method. We estimate and compensate multipath channel through the proposed adaptive channel estimation method. The BER performance of the proposed method is better about 0.5 dB than the conventional method at the Vehicular A channel.