• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.150-158
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• 2013

In this paper we present approximated constrained least squares filter for real-time directionally adaptive image restoration. The proposed method makes a hardware implementation easier for real-time image restoration because of reducing the filter size. Furthermore, for directional adaptive image restoration, this paper estimates the local orientation by analyzing the covariance matrix and applies to approximated constrained least squares filter. Experimental results show that the proposed method is sharper and less artifacts than existing methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.95-104
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• 2016

Subspace-based direction-of-arrival (DOA) estimation algorithms have a difficulty in dealing with coherent signals caused by multi-path environment. As one of attempts to solve this problem, a spatial differencing method is known to be useful for not only estimating DOAs of the coherent signals but also improving the number of resolvable wavefronts even more than the number of antenna elements. However, since the conventional spatial differencing method uses only the partial statistics of the observed data, this method suffers from the performance degradation in estimation accuracy caused by the residual correlation between the uncorrelated signals. To cope with this problem, in this paper, a generalized spatial differencing method is proposed. Unlike the conventional method, the proposed method utilizes the entire statistics of the received signals. Therefore, the additional performance enhancement in both estimation accuracy and the number of resolvable wavefronts can be achieved. The performance analyses with computer simulations show that the proposed method outperforms the conventional method in terms of the estimation accuracy and the number of resolvable wavefronts.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.303-308
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• 2013

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.323-332
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• 2007

We present a new type of portmanteau test in the frequency domain which is derived from the discrete cosine transform(DCT). For the stationary time series, DCT coefficients are asymptotically independent and their variances are expressed by linear combinations of autocovariances. The covariance matrix of DCT coefficients for white noises is diagonal matrix whose diagonal elements is the variance of time series. A simple way to test the independence of time series is that we divide DCT coefficients into two or three parts and then compare sample variances. We also do this by testing the slope in the linear regression model of which the response variables are absolute values or squares of coefficients. Simulation results show that the proposed tests has much higher powers than Ljung-Box test in most cases of our experiments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.5
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• pp.405-412
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• 2015

This paper represents a filter design to estimate the airspeed of a spin-stabilized, trajectory-correctible artillery ammunition. Due to the limited power and space in operational point of view, the airspeed sensor is not installed, and thus the airspeed need to be estimated using limited sensor measurements. The only IMU measurements(three-axis specific forces and angular rates) are used in this application. The extended Kalman filter algorithm is applied since a linear filter can not cover the its wide operational range in airspeed and altitude. In the implementation of the EKF, the state and measurement equations are transformed into the no-roll frame for simple form of Jacobian matrix. The simulation study is conducted to evaluate the performance of the filter under various environment conditions of sensor noise and wind turbulence. In addition, the effect of the choice in filter design parameters, i.e. process error covariance matrices is analyzed on the performance of the estimation of airspeed and angular rates.

• Spatial Information Research
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• v.21 no.2
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• pp.45-54
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• 2013

Analytical and simulation error models have the ability to describe (or realize) error-corrupted versions of spatial data. But the different approaches for modeling positional errors require an internal validation that ascertains whether the analytical and simulation error models predict correct positional errors in a defined set of conditions. This paper presents stochastic simulation models of a point and a line segm ent to be validated w ith analytical error models, which are an error ellipse and an error band model, respectively. The simulation error models populate positional errors by the Monte Carlo simulation, according to an assumed error distribution prescribed by given parameters of a variance-covariance matrix. In the validation process, a set of positional errors by the simulation models is compared to a theoretical description by the analytical error models. Results show that the proposed simulation models realize positional uncertainties of the same spatial data according to a defined level of positional quality.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.40-46
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• 2008

Weak target detection and localization in the presence of loud surface ship noise is a critical problem for matched field processing (MFP) in shallow water. For stationary sources, each signal component of received signal can be separated and interference can be suppressed using eigen space analysis schemes. However, source motion, in realistic cases, causes spreading of signal energies in their subspace. In this case, eigenvalues of target and interfere signal components are mixed and hard to be separated with usual phone space eigenvector decomposition (EVD) approaches. Our technique is based on mode space and utilizes the difference in their physical characteristics of surface and submerged sources. Performing EVD for modal cross spectral density matrix, interference components in the mode amplitude subspace can be classified and eliminated. This technique is demonstrated with synthetic data, and results are discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.153-158
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• 2020

In this paper, we studied an algorithm for estimating a desired target by removing noise and interference in a wireless communication environment. When an information signal with a mixture noise and interference receive a receiver, noise and interference signals must be removed to accurately estimate a desired target. In order to divide the received signal region into two spatial, a power spectrum is obtained by analyzing a correlation matrix, covariance, eigen vector, and eigen value. The proposed spectrum is an algorithm that can remove noise and interference, and analyzes the existing algorithm and target estimation performance through simulation. As a result of simulation, the target estimation resolution of existing algorithm is more than 10°, but the resolution of the proposed algorithm is less than 10°. The proposed algorithm has improved the resolution of about 5° than the exiting algorithm. The proposed algorithm proved that the target estimation accuracy and resolution are superior to the existing algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• The Journal of the Acoustical Society of Korea
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• v.23 no.3
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• pp.206-213
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• 2004

This paper presents a steering angle error compensation (SAEC) algorithm that is appropriate for rapidly moving sources. The Proposed algorithm utilizes a modal covariance matrix from multiple frequency components instead of the multiple snapshots in a narrowband SAEC, and estimates the steering error by maximizing the wideband WVDR output power using a first-order Taylor series approximation of the modal steering vector in terms of the steering error. As such, the steering error can be compensated with short observation times. Several simulations using artificial and sea trial data are used to demonstrate the Performance of the proposed algorithm.