• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.522-522
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• 2000

In this paper, two pre-filtering techniques are presented for accurately estimating the impact location of a loose part. The reason why a pre-filterng technique Is necessary in a Loose Part Monitoring System is that the effects of background noise on the signal to noise ratio (SNR) can be reduced considerably resulting in improved estimation accuracy. The first method is to take d moving average operation in the time domain. The second one is to adopt band-pass filters designed in the frequency domain such as a Butterworth filter, Chebyshev filter I & II and an Elliptic Filter. To show the effectiveness, the impact test data (signals) from the YGN3 power plant are first preprocessed and then used to estimate the loose pan impact position. Resultantly. we observed that SNR is much improved and the average estimation error is below 7.5%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.378-381
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• 2006

This paper proposes the method of a channel estimation for MIMO-OFDM with ISI. The proposed method uses a new special training sequence to obtain a constant PAR in OFDM and to remove the effect of ISI on channel estimation. Using this training sequence, we are able to avoid a singular problem in matrix. As a result of simulation, we are able to assure that the proposed system inclosed the performance in MSE of estimated channel by more than 30dB than a conventional method if SNR is high.

• Journal of Advanced Navigation Technology
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• v.24 no.1
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• pp.47-52
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• 2020

Direction of arrival (DoA) estimation is a scheme of estimating the directions of targets by analyzing signals generated or reflected from the targets and is used in various fields. Artificial neural networks (ANN) is a field of machine learning that mimics the neural network of living organisms. They show good performance in pattern recognition. Although researches has been using ANNs to estimate the DoAs, there are limitationsin dealing with variations of the signal-to-noise ratio (SNR) of the target signals. In this paper, we propose a three-stage ANN algorithm for DoA estimation. The proposed algorithm can minimize the performance reduction by applying the model trained in a single SNR environment to various environments through a 'noise reduction process'. Furthermore, the scheme reduces the difficulty in learning and maintains efficiency in estimation, by employing a process of DoA shift. We compare the performance of the proposed algorithm with Cramer-Rao bound (CRB) and the performances of existing subspace-based algorithms and show that the proposed scheme exhibits better performance than other schemes in some severe environments such as low SNR environments or situations in which targets are located very close to each other.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.2A
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• pp.148-158
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• 2008

This paper proposes an efficient signal to noise ratio (SNR) estimation algorithm and its hardware implementation for adaptive transmission system using M-ary modulation scheme. In this paper, we present the implementation results of the proposed algorithm for the second generation digital video broadcasting via satellite (DVB-S2) system, and the proposed algorithm can be tailored to the other communication systems using adaptive transmissions. We built a look-up table (LUT) using the theoretical background of the received signal distribution, and by using this LUT we need just two comparators and a counter for the hardware implementation. For this reason, the hardware of the proposed scheme produces accurate estimation results even with extremely low complexity. The simulation results investigated in this paper reveal that the proposed method can produce estimation results within the specified SNR range in the DVB-S2 system, and it requires a few hundreds of samples for average estimation error of about 1 dB.

• Speech Sciences
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• v.14 no.4
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• pp.145-156
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• 2007

The performance of speaker identification is severely degraded in noisy environments. Recently probability weighting method based on observation membership was proposed for overcoming the noise problem[1]. In the paper[1] the observation confidence was calculated from SNR with sigmoid function. However, estimating SNR needs additive calculation amount and estimated SNR is corrupted in dynamic noisy environments. In this paper we propose estimation methods of the observation confidence based on score-based reliabilities (SBR) of entropy and dispersion measures. Generally SBRs are obtained from speaker models' probabilities. The proposed methods are evaluated with ETRI speaker recognition DB. We compared the performances of the proposed methods with those in [1][8]. The experimental results show that the proposed methods can be successfully applied for the case where SNR is not available.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.125-133
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So intuitive fuzzy c-means(IFCM) clustering algorithm is applied to improve KNN, which is the KNN/IFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of IFCM based on signal to noise ratio(SNR). Then, the k RPs are classified into different clusters through IFCM based on SNR. Experimental results indicate that the proposed KNN/IFCM hybrid algorithm generally outperforms KNN, KNN/FCM, KNN/PFCM algorithm when the locations error is less than 2m.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.109-115
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• 2011

As fingerprinting method, k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighbors k and positions of reference points(RPs). So artificial neural network(ANN) clustering algorithm is applied to improve KNN, which is the KNN/ANN hybrid algorithm presented in this paper. For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of ANN based on SNR. Then, the k RPs are classified into different clusters through ANN based on SNR. Experimental results indicate that the proposed KNN/ANN hybrid algorithm generally outperforms KNN algorithm when the locations error is less than 2m.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7A
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• pp.702-709
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• 2008

This paper presents a frequency offset estimation scheme which can be used for packet based OFDM communication systems. The proposed scheme detects the failure of performing coarse frequency offset estimation and compensates the error of estimated coarse frequency offset. The preamble structure considered in this paper is based on the preamble specified in IEEE802.11a and IEEE802.11p standards. We performed simulation to compare the performance according to the different number of reference packets used to detect the failure of performing coarse frequency offset estimation. The simulation results show that the proposed scheme has better performance than the conventional scheme in the low SNR(below 2dB) environment.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.10
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• pp.8-12
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• 2007

In this paper, we proposed an frequency offset estimation scheme which can be used for packet based OFDM communication systems such as IEEE802.11a and IEEE802.11p physical layer. Proposed estimation scheme can adjust the gain allocation ratio between long training symbol and short training symbol while maintaining average power of overall training sequence so that we can obtain the reference parameters for MSE performance improvement. The preamble structure considered in this paper is based on the preamble specified in IEEE802.11a and IEEE802.11p standardization group. From the simulation results, it is shown that power ratio between long training symbol and short training symbol must vanes for achieving lower frequency offset estimation error as channel SNR condition is changed. Also it is known oat proposed scheme can achieve better performance than conventional one.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.238-242
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.