• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.41-47
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• 2024

With the advancement and proliferation of the Internet of Things (IoT), a wide range of location-based services are being offered, and various ranging methods are being researched to meet the objectives of the required services. Conventional ranging methods involve the direct exchange of signals between tags and anchors to estimate distance, presenting a limitation in efficiently utilizing communication resources in large-scale IoT environments. To overcome these limitations, active-passive ranging methods have been proposed. However, there is a lack of theoretical convergence guarantees against clock drift errors and a detailed analysis of the characteristics of ranging estimation techniques, making it challenging to derive precise positioning results. In this paper, an improved active-passive ranging method that accounts for clock drift errors is proposed for precise positioning in large-scale IoT environments. The simulation results confirmed that the proposed active-passive ranging method can enhance distance estimation performance by up to 94.4% and 14.4%, respectively, compared to the existing active-passive ranging methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.344-346
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• 2019

In this paper, we propose a distance estimation method using the convolutional neural network in Ultra-Wideband (UWB) systems. The training data set used to learn the deep learning model using the convolutional neural network is generated by the MATLAB program and utilizes the IEEE 802.15.4a standard. The performance of the proposed distance estimation method is verified by comparing the threshold based distance estimation technique and the performance comparison used in the conventional distance estimation.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.71-76
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• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.778-785
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• 2010

This paper proposes a high-precision ranging scheme based on channel estimation technique and multipath delay analysis in IR-UWB systems. When the IR-UWB signal is transmitted and received, the high-precision ranging is estimated with the time-of-arrival information of the signal. In the proposed scheme, the channel estimation process with the minimum mean square error technique or zero forcing technique is performed and the overlapped multipath within the pulse is analyzed with matrix pencil (MP) algorithm to achieve the ranging accuracy of centimeters. The performance of proposed scheme is evaluated with various IEEE 802.15.4a channel models and the relationship between the ranging performance and the computational complexity is analyzed in terms of the MP parameter values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.104-113
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• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.52-57
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• 2001

The passive ranging estimation techniques using a focused beamformer have been studied under the water. It is well known that the passive ranging estimation method using a focused beamformer is excellently evaluated. Among these, the passive ranging sonar is known to have a good performance under low signal-to-noise. ratio. However, its performance is degraded in multi-source environments. In this paper, we proposed the technique using dual focused beamformers to estimate the range. And when the sampling frequency is low, it is very difficult to steer the focused beam to the desired direction, as a result of this, the low performance occurs because of a distorted beam pattern. In this paper, we study the effect of sampling rate on passive ranging by using focused beamformer. And we verified the performance of the proposed method via computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1290-1297
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• 2019

The paper proposes a distance estimation technique for ultra-wideband (UWB) systems using convolutional neural network (CNN). To estimate the distance from the transmitter and the receiver in the proposed method, 1 dimensional vector consisted of the magnitudes of the received samples is reshaped into a 2 dimensional matrix, and by using this matrix, the distance is estimated through the CNN regressor. The received signal for CNN training is generated by the UWB channel model in the IEEE 802.15.4a, and the CNN model is trained. Next, the received signal for CNN test is generated by filed experiments in indoor environments, and the distance estimation performance is verified. The proposed technique is also compared with the existing threshold based method. According to the results, the proposed CNN based technique is superior to the conventional method and specifically, the proposed method shows 0.6 m root mean square error (RMSE) at distance 10 m while the conventional technique shows much worse 1.6 m RMSE.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.367-370
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• 2000

Various methods of enhancing the performance of passive range sonar arrays have been discussed, triangulation, wavefront curvature method etc. But they are not appropriate to the methods because of very low SNR in underwater environment. We made appropriate sub-arrays in a linear array and applied to the beamformers such as a minimum variance with null constraints.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.5
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• pp.637-642
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• 2011

In this paper, a new technique to estimate the distances between a vanishing point and moving objects is proposed. A vanishing point for an input image is estimated and it use to extract distance form the vanishing point to a moving object. Using the obtained distances, moving objects is extracted. In simulation results, several performances for a test image sequnce is shown.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.