• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• Speech Sciences
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• v.15 no.3
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• pp.17-28
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• 2008

In many practical applications of robots, finding the location of an incoming sound is an important issue for the development of efficient human robot interface. Most sound source localization algorithms make use of only those microphones that are acoustically visible from the sound source or do not take into account the effect of sound diffraction, thereby degrading the sound source localization performance. This paper proposes a new sound source localization method that can utilize those microphones that are acoustically shadowed from the sound source. The experiment results show that use of the acoustically shadowed microphones, which receive higher signal-to-noise ratio signals than the others and are closer to the sound source, improves the performance of sound source localization.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.610-618
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• 2011

For improved localization around the indoor/outdoor transit area of buildings, this paper proposes an efficient algorithm combining the measurements from the WLAN (Wireless Local Area Network) and the GPS (Global Positioning System) for. The proposed hybrid localization algorithm considers both multipath errors and NLOS (Non-Line-of-Sight) errors, which occur in most wireless localization systems. To detect and isolate multipath errors occurring in GPS measurements, the propose algorithm utilizes conventional multipath test statistics. To convert WLAN signal strength measurements to range estimates in the presence of NLOS errors, a simple and effective calibration algorithm is designed to compute conversion parameters. By selecting and combining the reliable GPS and WLAN measurements, the proposed hybrid localization algorithm provides more accurate location estimates. An experiment result demonstrates the performance of the proposed algorithm.

• Smart Structures and Systems
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• v.8 no.2
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• pp.219-237
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• 2011

This paper presents novel research into the source localization of multiple impacts. Source localization technology for single impact loads in a plate structure has been used for health monitoring. Most of research on source localization has been focused only on the localization of single impacts. Overlapping of dispersive waves induced by multiple impacts and reflection of those waves from the edge of the plate make it difficult to localize the sources of multiple impacts using traditional source localization technology. The method solving the overlapping problem and the reflection problem is presented in the paper. The suggested method is based on pre-signal processing technology using band pass filter and optimal filter. Results from numerical simulation and from experimentation are presented, and these verify the capability of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.1
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• pp.9-15
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• 2016

Source localization has developed in various fields of signal processing including radar, sonar, and wireless communication, etc. Source localization can be found by estimating the time difference of arrival between the each of sensors. Several methods like the NLS(Nonlinear Least Square) cost function have been proposed in order to improve the performance of time delay estimation. In this paper, we propose a clustering method using the four sensors with the same aperture as previous methods of using the three sensors. Clustering method can be improved the source localization performance by grouping similar estimated values. The performance of source localization using clustering method is evaluated by Monte Carlo simulation.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1E
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• pp.8-13
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• 2006

In this paper, an Optimized Ambisonic Panning Algorithm (OAPA) which reduces sound localization error, is proposed. In the conventional Ambisonic Panning Algorithm (APA), sound localization is usually different from the panning angle, especially when listeners are not in an ideal listening position, because of low signal separation among other channels. To overcome this problem, an OAPA using window functions is proposed. A proper window function can be verified, comprising of higher harmonic components than 2M+1 and improved DPC and channel separation. Analysis results demonstrate that the proposed method results in higher signal separation among other channels and lower sound localization errors than the conventional APA.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.23-30
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• 2011

In the wireless sensor network(WSN), the detection of precise location of sensor nodes is essential for efficiently utilizing the sensing data acquired from sensor nodes. Among various location methods, the received signal strength (RSS) based localization scheme is mostly preferable in many applications since it can be easily implemented without any additional hardware cost. Since the RSS localization method is mainly effected by radio channel between two nodes, outlier data can be included in the received signal strength measurement specially when some obstacles move around the link between nodes. The outlier data can have bad effect on estimating the distance between two nodes such that it can cause location errors. In this paper, we propose a RSS-based localization method using Robust Statistic and Gaussian filter algorithm for enhancing the accuracy of RSS-based localization. In the proposed algorithm, the outlier data can be eliminated from samples by using the Robust Statistics as well as the Gaussian filter such that the accuracy of localization can be achieved. Through simulation, it is shown that the proposed algorithm can increase the accuracy of localization and is more robust to non gaussian noise channels.

• ETRI Journal
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• v.37 no.4
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• pp.707-716
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• 2015

The purpose of a localization system is to estimate the coordinates of the geographic location of a mobile device. The accuracy of wireless localization is influenced by nonline-of-sight (NLOS) errors in wireless sensor networks. In this paper, we present an improved time of arrival (TOA)-based localization method for wireless sensor networks. TOA-based localization estimates the geographic location of a mobile device using the distances between a mobile station (MS) and three or more base stations (BSs). However, each of the NLOS errors along a distance measured from an MS (device) to a BS (device) is different because of dissimilar obstacles in the direct signal path between the two devices. To accurately estimate the geographic location of a mobile device in TOA-based localization, we propose an optimized localization method with a BS selection scheme that selects three measured distances that contain a relatively small number of NLOS errors, in this paper. Performance evaluations are presented, and the experimental results are validated through comparisons of various localization methods with the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.182-188
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• 2014

Source localization is an important problem in the field of sonar and radar, etc. For the purpose of source localization, two or more spatially separated sensors are often used to measure the time difference of arrivals of a radiating source whose transmitted signal waveform is unknown. The NLS(Nonlinear Least Square) cost function with curve fitting method was proposed recently, which provide robust source localization performance by reducing estimation ambiguity. However, even this algorithm shows degraded performance in a multipath environment. To estimates source localization correctly, source localization algorithm that eliminate the effect of multipath signals is required. In this paper, PMP(Power Matching Procedure) is added to the algorithm, which provides improved source localization performance by properly cutting out the effect of multipath signals. Through simulation the performance of the proposed source localization algorithm is verified.