• Journal of information and communication convergence engineering
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• v.10 no.4
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• pp.349-358
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• 2012

Over a wireless sensor network (WSN), accurate localization of sensor nodes is an important factor in enhancing the association between location information and sensory data. There are many research works on the development of a localization algorithm over three-dimensional (3D) space. Recently, the complexity-reduced 3D trilateration localization approach (COLA), simplifying the 3D computational overhead to 2D trilateration, was proposed. The method provides proper accuracy of location, but it has a high computational cost. Considering practical applications over resource constrained devices, it is necessary to strike a balance between accuracy and computational cost. In this paper, we present a novel 3D localization method based on the received signal strength indicator (RSSI) values of four anchor nodes, which are deployed in the initial setup process. This method provides accurate location estimation results with a reduced computational cost and a smaller number of anchor nodes.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.67-71
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• 2016

This paper addresses a problem of defocus map recovery from single image. We describe a simple effective approach to estimate the spatial value of defocus blur at the edge location of the image. At first, we perform a re-blurring process using Gaussian function with input image, and calculate a gradient magnitude ratio with blurring amount between input image and re-blurred image. Then we get a full defocus map by propagating the blur amount at the edge location. Experimental result reveals that our method outperforms a reliable estimation of depth map, and shows that our algorithm is robust to noise, inaccurate edge location and interferences of neighboring edges within input image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.11
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• pp.884-890
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• 2018

In this paper, an improved method of estimating static source location is proposed based on the direct position determination(DPD) method, which estimates a source position directly using received signals. When the source position is estimated using the conventional DPD method, the estimation accuracy and error depend on a pair of receivers: a reference receiver and one of the multiple moving receivers. Based on this, the weighting values of the estimating source location were obtained using the covariance matrix for the pair of receivers($S_1$, $S_{2i}$) and applied to the DPD algorithm. Finally, the source position was estimated using the proposed DPD algorithm, and it was verified that the estimation accuracy improved, compared to the conventional DPD algorithm.

• Transactions on Electrical and Electronic Materials
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• v.14 no.4
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• pp.187-192
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• 2013

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.1-8
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• 2016

In this study, an WiFi fingerprinting method based on the k-nn algorithm was applied to improve the accuracy of location tracking of a moving train on a platform and evaluate the performance to minimize the estimation error of location tracking. The data related to the position of the moving train are monitored by the control center for trains and used widely for the safety and comfort of passengers. The train location tracking methods based on WiFi installed by telecom companies were evaluated. In this study, a simulator was developed to consider the environments of two cases; in already installed WiFi devices and new installed WiFi devices. The developed simulator can simulate the localized estimation of the position under a variety of conditions, such as the number of WiFi devices, the area of platform and entry velocity of train. To apply location tracking algorithms, a k-nn algorithm and fuzzy k-nn algorithm were applied selectively according to the underlying condition and also four distance measurement algorithms were applied to compare the error of location tracking. In conclusion, the best method to estimate train location tracking is a combination of the k-nn algorithm and Minkoski distance measurement at a 0.5m grid unit and 8 WiFi AP installed.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.81-88
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• 2016

In this paper, we propose an adaptive multi-target estimation algorithm using the characteristics of signals in the IR-UWB(Impulse-Radio Ultra Wideband) radar system, which is attracting attention because it has good transparency, robustness to the indoor environment, and high precision positioning of tens of centimeters. We proposed an algorithm that estimates multiple peaks with the characteristic that the signal reflected by the target has a peak. To verify the performance of these algorithms, multiple targets were placed in front of the radar and the existing technique and the multi - target estimation algorithm were compared. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. The number of estimates can be increased compared with the existing peak signal derivation method, and multiple targets can be derived. The conventional technique estimates only one target, which results in a mean square error of 1 while a multi - target estimation algorithm yields a result of about 0.05. The proposed method is expected to be able to apply multiple targets to the estimation and application in one IR-UWB module environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5860-5876
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• 2018

A single snapshot data can only provide limited amount of information so that the rank of covariance matrix is not full, which is not adopted to complete the parameter estimation directly using the traditional super-resolution method. Aiming at solving the problem, a joint time delay and angle estimation using matrix pencil method based on information reconstruction vector for orthogonal frequency division multiplexing (OFDM) signal is proposed. Firstly, according to the channel frequency response vector of each array element, the algorithm reconstructs the vector data with delay and angle parameter information from both frequency and space dimensions. Then the enhanced data matrix for the extended array element is constructed, and the parameter vector of time delay and angle is estimated by the two-dimensional matrix pencil (2D MP) algorithm. Finally, the joint estimation of two-dimensional parameters is accomplished by the parameter pairing. The algorithm does not need a pseudo-spectral peak search, and the location of the target can be determined only by a single receiver, which can reduce the overhead of the positioning system. The theoretical analysis and simulation results show that the estimation accuracy of the proposed method in a single snapshot and low signal-to-noise ratio environment is much higher than that of Root Multiple Signal Classification algorithm (Root-MUSIC), and this method also achieves the higher estimation performance and efficiency with lower complexity cost compared to the one-dimensional matrix pencil algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.71-76
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• 2019

Research on indoor location tracking technology using smartphone is actively being carried out. Especially, the movement distance of the smartphone should be accurately measured and the movement route of the user should be displayed on the map. Location tracking technology using sensors mounted on smart phones has been used for a long time, but accuracy is not good enough to measure the moving distance of the user using only the sensor. Therefore, when the user moves the smartphone in a certain posture, it must research and develop an appropriate algorithm to measure the distance accurately. In this paper, we propose a method to reduce moving distance estimation error by removing user 's foot shape by limiting the screen of smartphone in pyramid - based optical flow estimation method.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.232-237
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• 2023

This paper presents a system for estimating the position of a magnet using a magnetic sensor. An algorithm is presented to analyze the waveform and output voltage values of the magnetic field generated at each position when the magnet moves and to estimate the position of the magnet based on the analyzed data. Here, the magnet is sufficiently small to be inserted into a blood vessel and has a micro-magnetic field of hundreds of nanoteslas owing to the small size and shape of the guide wire. In this study, a highly sensitive magneto-impedance (MI) sensor was used to detect these micro-magnetic fields. Nine MI sensors were arranged in a 3×3 configuration to detect a magnetic field that changes according to the position of the magnet through the MI sensor, and the voltage value output was polynomially regressed to specify a position value for each voltage value. The accuracy was confirmed by comparing the actual position value with the estimated position value by expanding it from a 1D straight line to a 3D space. Additionally, we could estimate the position of the magnet within a 3% error.