• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2879-2890
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• 2020

In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.445-453
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• 2001

The multiple targets tracking problem has been one of the main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimen-sion filter, input estimation filter, interacting multiple model(IMM) filter, dederated variable dimension filter with input estimation, etc., have proposed to address the tracking and sensor fusion issues. In this pa- per, two existing tracking algorithm, i.e, the IMM filter and the variable dimension filter with input estima-tion(VDIE), are combined for the purpose of improving the tracking performance for maneuvering targets. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns, i.e., waver, pop-up, and high-diver motions, are defined and are applied to the modified IMM filter as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMM filter than the standard IMM filter are demonstrated though computer simulations.

• Smart Structures and Systems
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• v.12 no.6
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• pp.619-640
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• 2013

It is highly desirable to explore efficient algorithms for detecting structural damage of large size structural systems with limited input and output measurements. In this paper, a new structural damage detection algorithm based on substructure approach is proposed for large size structural systems with limited input and output measurements. Inter-connection effect between adjacent substructures is treated as 'additional unknown inputs' to substructures. Extended state vector of each substructure and its unknown excitations are estimated by sequential extended Kalman estimator and least-squares estimation, respectively. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements on the substructure interface DOFs, which is superior to previous substructural identification approaches. Also, structural parameters and unknown excitation are estimated in a sequential manner, which simplifies the identification problem compared with other existing work. Structural damage can be detected from the degradation of the identified substructural element stiffness values. The performances of the proposed algorithm are demonstrated by several numerical examples and a lab experiment. Measurement noise effect is considered. Both the simulation results and experimental data validate that the proposed algorithm is viable for structural damage detection of large size structural systems with limited input and output measurements.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.779-784
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• 2008

This paper presents a the method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. By coherence function method, it is found that the biggest part of the noise source in the road noise is generated by structural vibration on the mechanical-acoustic transfer functions of vehicles. This analysis is modeled as four input/single output system because the noise is generated with four wheels that mechanism of the road noise is very complicated. The coherence function method is proved to be useful tool for identifying of noise source. The overall levels of the interior noise be coherence function method are compared with those measured and calculated by the frequency response function approach using mechanical excitation test. The experimental results have shown a good agreement with the results calculated by the coherence function method when the input sources are coherent strongly each other. The estimation of the road noise indicates that significant coherent can be achieved in the vehicle interior noise.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.949-954
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• 2017

Multiple input, multiple output orthogonal frequency division multiplexing (MIMO OFDM) systems are the candidate for the future wireless communications. However, the main drawback of MIMO OFDM systems is their sensitivity to carrier frequency offset (CFO) similar to the single input, single output OFDM (SISO OFDM) systems. The demodulation of a signal with CFO causes large bit error rate and degrade the performance of a symbol synchronizer. It is important to estimate the frequency offset and minimize or eliminate its impact. In this paper, we propose a technique based on observation training symbols for estimating CFO by employing block-by-block estimation for SISO OFDM systems. The technique of SISO OFDM is extended to the MIMO OFDM systems. Simulation results show that the proposed techniques have a superior performance and better accuracy compared to the conventional techniques in the sense of mean square error.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1206-1212
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• 2008

This paper presents a the method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. By coherence function method, it is found that the biggest part of the noise source in the road noise is generated by structural vibration on the mechanical-acoustic transfer functions of vehicles. This analysis is modeled as four input/single output system because the noise is generated with four wheels that mechanism of the road noise is very complicated. The coherence function method is proved to be useful tool for identifying of noise source. The overall levels of the interior noise be coherence function method are compared with those measured and calculated by the frequency response function approach using mechanical excitation test. The experimental results have shown a good agreement with the results calculated by the coherence function method when the input sources are coherent strongly each other. The estimation of the road noise indicates that significant coherent can be achieved in the vehicle interior noise.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.220-225
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• 2020

This paper investigates the non-intrusive speech intelligibility estimation method in noise environments when the bottleneck feature of autoencoder is used as an input to a neural network. The bottleneck feature-based method has the problem of severe performance degradation when the noise environment is changed. In order to overcome this problem, we propose a novel non-intrusive speech intelligibility estimation method that adds the noise environment information along with bottleneck feature to the input of long short-term memory (LSTM) neural network whose output is a short-time objective intelligence (STOI) score that is a standard tool for measuring intrusive speech intelligibility with reference speech signals. From the experiments in various noise environments, the proposed method showed improved performance when the noise environment is same. In particular, the performance was significant improved compared to that of the conventional methods in different environments. Therefore, we can conclude that the method proposed in this paper can be successfully used for estimating non-intrusive speech intelligibility in various noise environments.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.90-96
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• 1995

A new method for autoregressive parameter estimation from noisy observation sequence is presented. This method, termed the AP method, is a result of an attempt to make use of the adaptive predictor which is a simple and reliable way of parameter estimation. It is shown theoretically that, for noisy input, the parameter vector computed from the prediction sequence is closer to that of the original sequence than the noisy input sequence is, under the spectral distortion criterion. Simulation results with the Kalman filter as a noise reduction filter and real speech data supported the theory. Roughly speaking, the performance of the parameter set obtained by the AP method is better than noisy one but worse than the EM iteration results. When the simplicity is considered, it could provide a useful alternative to more complicated parameter estimation methods in some applications.

• Journal of Advanced Navigation Technology
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• v.15 no.5
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• pp.735-741
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• 2011

In this paper, we have analyzed the effect of SNR estimation error on the BER performance of MMSE-DFE in high-speed binary CDMA system. Since MMSE equalization algorithm requires the SNR value of input signal, it should be estimated using CAZAC sequence in preamble. However, when AWGN and ISI exist simultaneously, it is impossible to estimate the exact SNR value of input signal and thereby equalizer's performance may be deteriorated. The simulation results can be used as a guideline for selection of SNR estimation algorithm for MMSE-DFE design.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.1-21
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• 2016

The Markowitz portfolio selection model uses estimators to deduce input parameters. However, the estimation errors of input parameters negatively influence the performance of portfolios. Therefore, this model cannot be reliably applied to real-world investments. To overcome this problem, we suggest an algorithm that can exclude stocks with large estimation error from the portfolio by applying a tracking signal to the Markowitz portfolio selection model. By calculating the tracking signal of each stock, we can monitor whether unexpected departures occur on the outcomes of the forecasts on rate of returns. Thereafter, unreliable stocks are removed. By using this approach, portfolios can comprise relatively reliable stocks that have comparatively small estimation errors. To evaluate the performance of the proposed approach, a 10-year investment experiment was conducted using historical stock returns data from 6 different stock markets around the world. Performance was assessed and compared by the Markowitz portfolio selection model with additional constraints and other benchmarks such as minimum variance portfolio and the index of each stock market. Results showed that a portfolio using the proposed approach exhibited a better Sharpe ratio and rate of return than other benchmarks.