• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.71-78
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• 2024

In indoor environments, since global positioning system (GPS) signals can be blocked by obstacles, such as building structure. the performance of GPS-based positioning methods can be degraded because of the loss of GPS signals. To solve this problem, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope, accelerometer, and magnetometer, have been proposed to enhance the positioning accuracy in indoor environments. IMU-based positioning methods can estimate the location of the user by calculating the velocity and heading angle of the user without the help of GPS. However, low-cost MEMS IMUs may lead to drift error and large bias. In addition, positioning errors in IMU-based positioning approaches can be caused by the irrelevant motion of the pedestrian. In this study, we propose an enhanced indoor positioning method that provides more reliable localization results by using the camera, light detection and right (LiDAR), and ARKit framework on the iPhone. Through reliable positioning results and augmented reality (AR) experiences, our indoor positioning system can provide indoor space guidance services.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.76-84
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• 2013

In the passive emitter localization using instantaneous TDOA (time difference of arrival) and FDOA (frequency difference of arrival) measurements, the estimation accuracy can be improved by collecting additional measurements. To achieve this goal, it is required to increase the number of the sensors. However, in electronic warfare environment, a large number of sensors cause the loss of military strength due to high probability of intercept. Also, the additional processes should be considered such as the data link and the clock synchronization between the sensors. Hence, in this paper, the passive localization of a stationary emitter is presented by using the successive TDOA and FDOA measurements from two moving sensors. In this case, since an independent pair of sensors is added in the data set at every instant of measurement, each pair of sensors does not share the common reference sensor. Therefore, the QCLS (quadratic correction least squares) methods cannot be applied, in which all pairs of sensor should include the common reference sensor. For this reason, a Gauss-Newton algorithm is adopted to solve the non-linear least square problem. In addition, to show the performance of the proposed method, we compare the RMSE (root mean square error) of the estimates with CRLB (Cramer-Rao lower bound) and derived the CEP (circular error probable) planes to analyze the expected estimation performance on the 2-dimensional space.

• Smart Structures and Systems
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• v.8 no.3
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• pp.303-320
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• 2011

Comparing to active damage monitoring, impact localization on composite by using time reversal focusing method has several difficulties. First, the transfer function of the actuator-sensor path is difficult to be obtained because of the limitation that no impact experiment is permitted to perform on the real structure and the difficulty to model it because the performance of real aircraft composite is much more complicated comparing to metal structure. Second, the position of impact is unknown and can not be controlled as the excitation signal used in the active monitoring. This makes it not applicable to compare the difference between the excitation and the focused signal. Another difficulty is that impact signal is frequency broadband, giving rise to the difficulty to process virtual synthesis because of the highly dispersion nature of frequency broadband Lamb wave in plate-like structure. Aiming at developing a practical method for on-line localization of impact on aircraft composite structure which can take advantage of time reversal focusing and does not rely on the transfer function, a PZT sensor array based phase synthesis time reversal impact imaging method is proposed. The complex Shannon wavelet transform is presented to extract the frequency narrow-band signals from the impact responded signals of PZT sensors. A phase synthesis process of the frequency narrow-band signals is implemented to search the time reversal focusing position on the structure which represents the impact position. Evaluation experiments on a carbon fiber composite structure show that the proposed method realizes the impact imaging and localization with an error less than 1.5 cm. Discussion of the influence of velocity errors and measurement noise is also given in detail.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.350-355
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• 2017

UWB (Ultra Wide Band) refers to a system with a bandwidth of over 500 MHz or a bandwidth of 20% of the center frequency. It is robust against channel fading and has a wide signal bandwidth. Using the IR-UWB based ranging system, it is possible to obtain decimeter-level ranging accuracy. Furthermore, IR-UWB system enables acquisition over glass or cement with high resolution. In recent years, IR-UWB-based ranging chipsets have become cheap and popular, and it has become possible to implement positioning systems of several tens of centimeters. The system can be configured as one-way ranging (OWR) positioning system for fast ranging and TWR (two-way ranging) positioning system for cheap and robust ranging. On the other hand, the ranging based positioning system has a limitation on the number of terminals for localization because it takes time to perform a communication procedure to perform ranging. To overcome this problem, code multiplexing and channel multiplexing are performed. However, errors occur in measurement due to interference between channels and code, multipath, and so on. The measurement filtering is used to reduce the measurement error, but more fundamentally, techniques for removing these measurements should be studied. First, the TWR based positioning was analyzed from a stochastic point of view and the effects of outlier measurements were summarized. The positioning algorithm for analytically identifying and removing single outlier is summarized and extended to three dimensions. Through the simulation, we have verified the algorithm to detect and remove single outliers.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.149-156
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• 2008

SLAM(Simultaneous Localization and Mapping) system is to find a global position and build a map with sensing data when an unmanned-robot navigates an unknown environment. Two kinds of system were developed. One is used distance measurement sensors such as an ultra sonic and a laser sensor. The other is used stereo vision system. The distance measurement SLAM with sensors has low computing time and low cost, but precision of system can be somewhat worse by measurement error or non-linearity of the sensor In contrast, stereo vision system can accurately measure the 3D space area, but it needs high-end system for complex calculation and it is an expensive tool. In this paper, we implement the SLAM system using a single camera image and a PSD sensors. It detects obstacles from the front PSD sensor and then perceive size and feature of the obstacles by image processing. The probability SLAM was implemented using the data of sensor and image and we verify the performance of the system by real experiment.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1272-1280
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• 2013

In this paper, we propose an audio surveillance system which can detect and localize dangerous sounds in real-time. The location information about dangerous sounds can render a PTZ camera to be directed so as to catch a snapshot image about the dangerous sound source area and send it to clients instantly. The proposed audio surveillance system firstly detects foreground sounds based on adaptive Gaussian mixture background sound model, and classifies it into one of pre-trained classes of foreground dangerous sounds. For detected dangerous sounds, a sound source localization algorithm based on Dual delay-line algorithm is applied to localize the sound sources. Finally, the proposed system renders a PTZ camera to be oriented towards the dangerous sound source region, and take a snapshot against over the sound source region. Experiment results show that the proposed system can detect foreground dangerous sounds stably and classifies the detected foreground dangerous sounds into correct classes with a precision of 79% while the sound source localization can estimate orientation of the sound source with acceptably small error.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.581-587
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• 2010

In this study, we investigated the effect of the simplification for low frequency region in Head-Related Transfer Function(HRTF) on the sound localization. For this purpose, HRTF was measured and analyzed. The result in the standard deviation of HRTF showed that the directional dependence of low frequency was smaller than that of high frequency region, which means the possibility of simplification in the low frequency region. Simplification was performed by flattening of the low frequency amplitude characteristics with the insertion of the high-pass filter, whose cutoff frequency is given by boundary frequency. Auditory experiments were performed to evaluate the simplified HRTF. The result showed that direction perception was not influenced by the simplification of the frequency characteristics of HRTF for the error of sound localization. The rate of confusion for the front and back was not affected by the simplification of the frequency characteristics within 1kHz of HRTF. Finally, we made it clear that the sound localization was not affected by the simplification of frequency characteristics of HRTF within 1kHz. The result is expected to be utilized to reduce the size of speech information with no deterioration of the directional characteristics of the speech signal.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.196-205
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• 2018

This paper presents an algorithm for passive localization of a ship by applying the ray back-propagation technique to the ship radiation noise data. The previous method [S. H. Abadi, D. Rouseff and D. R. Dowling, J. Acoust. Soc. Am. 131, 2599-2610 (2012)] estimates the position of a sound source in the near-field environment with no array tilt by using the RBD (Ray-based Blind Deconvolution) and ray back-propagation techniques. However, when there exists an array tilt, the above method leads to a large position estimation error. In order to overcome the problem, this study proposes an algorithm that estimates the position of a sound source by correcting the array tilt using the RBD and ray back-propagation techniques. The proposed algorithm was verified by using the ship noise of SAVEX15 (Shallow-water Acoustic Variability EXperiment in 2015) experimental data.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.1
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• pp.69-74
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• 2016

A technique for improving estimation accuracy is introduced in order to locate the impact position of artillery shell during the weapon scoring test. Study on localization of impacts using acoustic measurement has been conducted and the usability of sensor array is verified with experiments. When the blast occurs above the ground in the firing range, the acoustic sensor above the ground can measure the directly propagated sound with the ground-reflected one. In this study, a method for reducing estimation error by using the reflection signal measurements based on the time difference of arrival method. Considering the reflection sound works as same as placing a virtual sensor symmetrically through the ground. This idea enables a virtual three-dimensional array configuration with a two-dimensional plane array above the ground as such. The time difference between the direct and the reflected propagations can be estimated using cepstrum analysis. Performance test has been made in the simulation experiment in the football size area.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.26-30
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• 2018

Planetary global localization is necessary for long-range rover missions in which communication with command center operator is throttled due to the long distance. There has been number of researches that address this problem by exploiting and matching rover surroundings with global digital elevation maps (DEM). Using conventional methods for matching, however, is challenging due to artifacts in both DEM rendered images, and/or rover 2D images caused by DEM low resolution, rover image illumination variations and small terrain features. In this work, we use train CNN discriminator to match rover 2D image with DEM rendered images using conditional Generative Adversarial Network architecture (cGAN). We then use this discriminator to search an uncertainty bound given by visual odometry (VO) error bound to estimate rover optimal location and orientation. We demonstrate our network capability to learn to translate rover image into DEM simulated image and match them using Devon Island dataset. The experimental results show that our proposed approach achieves ~74% mean average precision.