• 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 the Korea Institute of Military Science and Technology
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• v.24 no.6
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• pp.655-663
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• 2021

In this paper, DFT-Based channel estimation with channel response mirroring is proposed and analyzed. In General, pilot symbols for channel estimation in MIMO(Multi-Input Multi-Output) OFDM(Orthogonal Frequency-Division Multiplexing) Systems have a diamond shape in the time-frequency plane. An interpolation technique to estimate the channel response of sub-carriers between reference symbols is needed. Various interpolation techniques such as linear interpolation, low-pass filtering interpolation, cubic interpolation and DFT interpolation are employed to estimate the non-pilot sub-carriers. In this paper, we investigate the conventional DFT-based channel estimation for noise reduction and channel response interpolation. The conventional method has performance degradation by distortion called "edge effect" or "border effect". In order to mitigate the distortion, we propose an improved DFT-based channel estimation with channel response mirroring. This technique can efficiently mitigate the distortion caused by the DFT of channel response discontinuity. Simulation results show that the proposed method has better performance than the conventional DFT-based channel estimation in terms of MSE.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.227-230
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• 1996

The paper highlights the need for cautious least squares estimation when dealing with industrial applications of bilinear self-tuning control and indicates in qualitative terms the benefits of the approach over linear self-tuning control schemes. The cautious least squares algorithm is described and the use of cautious self-tuning in the context of both commissioning and implementation discussed.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.46-48
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• 2004

This paper presents a simple method of rotational and translational motion estimation for digital image stabilization. The scheme first computes the rotation center by taking least squares of selected local velocity vectors, and the rotational angle is found from special subset of motion vectors. And then translational motion can be estimated by the relation among movement of rotation center, rotation angle and translation movement. To show the effectiveness of our approach, the synthetic images are evaluated, resulting in better performance.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.114-128
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• 2005

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

• Journal of Biomedical Engineering Research
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• v.26 no.2
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• pp.87-93
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• 2005

A new method for real-time estimating left ventricular relaxation time constant (T) from the left ventricular (LV) pressure waveform, based on the isovolumic relaxation model, is proposed. The presented method uses a recursive least squares (RLS) algorithm to accomplish real-time estimation. A new criterion to detect the end-point of the isovolumic relaxation period (IRP) for the estimation of T is also introduced, which is based on the pattern analysis of mean square errors between the original and reconstructed pressure waveforms. We have verified the performance of the new method in over 4,600 beats obtained from 70 patients. The results demonstrate that the proposed method provides more stable and reliable estimation of τ than the conventional 'off-line' methods.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.394-398
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• 2016

In this paper, a fire detection system based on quantitative risk estimation is presented. Multiple sensors are used to build a comprehensive indicator that represents the risk of fire quantitatively. The proposed fire risk estimation method consists of two stages which determines the occurrence of fire and estimates the toxicity of the surveillance area. In the first stage, fire is reliably detected under diverse fire scenarios. The risk of fire is estimated in the second stage. Applying Purser's Fractional Effective Dose (FED) model which quantitates harmfulness of toxic gases, the risk of the surveillance area and evacuation time are calculated. A fire experiment conducted using four different types of combustion materials for the verification of the system resulted in a maximum error rate of 12.5%. By using FED calculation and risk estimation methods, the proposed system can detect various signs of fire faster than conventional systems.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.575-582
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• 2019

Advanced driving assist system can support safety of driver and passengers which may require vehicle dynamics states as well as road geometry. It is essential to have in real-time estimation of related variables and parameters. Among the road geometry parameters, road slope angle which can not be measured is essential parameter in pose estimation, adaptive cruise control and others on sag road. In this paper, Kalman filter based method for the estimation of the vehicle dynamics and road slope angle using a nonlinear vehicle model is proposed. It uses a combination of Kalman filter as Cascade Extended Kalman Filter. CEKF uses measured vehicle states such as yaw rate, longitudinal/lateral acceleration and velocity. Unknown vehicle parameters such as center of gravity and inertia are obtained by 2 D.O.F lateral model and experimentally. Simulation and Experimental tests conducted with commercialized vehicle dynamics model and real-car.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.119-133
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• 2009

The purpose of this research is to accurately estimate the location of a device using the received signal strength indicator (RSSI) of IEEE 802.11 WLAN for location tracking in indoor environments. For the location estimation method, we adopted the calibration model. By applying the Adaptive Zone Based K-NNSS (AZ-NNSS) algorithm, which considers the velocity of devices, this paper presents a 9% improvement of accuracy compared to the existing K-NNSS-based research, with 37% of the K-NNSS computation load. The accuracy is further enhanced by using a Kalman filter; the improvement was about 24%. This research also shows the level of accuracy that can be achieved by replacing a subset of the calibration data with values computed by a numerical equation, and suggests a reasonable number of calibration points. In addition, we use both the mean error distance (MED) and hit ratio to evaluate the accuracy of location estimation, while avoiding a biased comparison.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.98-103
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• 2009

Purpose: Correct estimation of Glomerular filtration rate (GFR) is very important for an accurate clinical assessment of the kidney function. This study compares four GFR markers, a serum creatinine-based estimation using MDRD formula, Cystatin-C, Cr-51 EDTA 2 samples and 6 samples. Materials and Methods: Serum creatinine concentrations, Cystatin-C serum concentrations and Cr-51 EDTA clearance are measured in 43 patients who received or donated kidney. Results: The correlation coefficient between serum based estimated GFR (MDRD) and Cr-51 EDTA 6 samples was 0.817 (p<0.01). The correlation coefficient between Cystatin-C based GFR and EDTA 6 samples was 0.7322 (p<0.01). Regression analysis showed a statistically significant correlation between Cr-51 EDTA 2 samples and 6 samples (r=0.971, p<0.01). Mean value and ${\pm}2SD$ for the difference between Cr-51 EDTA 2 samples and 6 samples were 4.7 mL/min and ${\pm}9.3$ respectively. Conclusions: The estimation of two samples Cr-51 EDTA showed that the method can be simplified by reducing blood samples without losing its high accuracy.