• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.9
• /
• pp.362-369
• /
• 2012

Recently, a target detection based on a distributed sensor network has been much studied in active sonar. Zhou et al. proposed a target localization method using line fitting based on a distributed sensor network which consists of low complexity sensors that only report binary detection results. This method has three advantages relative to ML estimator. First, there is no need to estimate propagation model parameters. Second, the computation is simple. Third, it only use sensors with "detection", which implies less data to be collected by data processing center. However, this method has larger target localization error than the ML estimator. In this paper, a target localization method which modifies Zhou's method is proposed for reducing the localization error. The modified method shows the performance improvement that the target localization error is reduced by 40.7% to Zhou's method in the point of RMSE.

• Journal of Positioning, Navigation, and Timing
• /
• v.10 no.1
• /
• pp.49-54
• /
• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.6
• /
• pp.640-651
• /
• 2016

We propose an effective ToA-based localization method considering accuracy in indoor environments. The purpose of the localization system is to estimate the coordinates of the geographic location of target device. In indoor environments, accurately estimating the location of a target device is not easy due to various errors. The accuracy of wireless localization is influenced by NLOS errors. ToA-based localization measures the location of a target device using the distances between a mobile device and three or more base stations. However, each of the NLOS errors along a distance estimated from a target device to a base station is different because of dissimilar obstacles. To accurately estimate the target's location, an optimized localization process is needed in indoor environments. In this paper, effective ToA-based localization method process is proposed for improving accuracy in wireless sensor networks. Performance evaluations are presented, and the experimental localization system results are proved through comparisons of various localization methods with the proposed methods.

• Korean Journal of Cognitive Science
• /
• v.15 no.3
• /
• pp.15-24
• /
• 2004

The perceived position of a flashing target object is generally biased towards the direction of eye movement when there is no reference around the target. Current research examined the localization accuracy of a flashing target relative to a static reference. The perceived location of the target relative to the reference was distorted and the pattern of perceptual distortion systematically depended on the position of the reference relative to the target. This kind of result was consistently observed regardless of the distance between the reference and the target and direction of pursuit eye movement. We have discussed how these results could he explained by the theories previously suggested to explain the localization of objects.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.6
• /
• pp.1291-1298
• /
• 2023

Surveillance reconnaissance sensor network is used for surveillance in wartime and area of operation. In this paper, we propose a target localization method using the detection signal strength of seismic sensors. Relay equipment calculates the target location using coordinate information and detection signal strength of the seismic sensors. Target localization error deviation due to environmental factors was minimized by subtracting the dynamic offset when calculating the target location. Field test shows improvement of target localization through reduction of errors. The average error was decreased to 3.62m. Up to 62% improved result was obtained compared to weighted centroid localization method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.7
• /
• pp.3480-3500
• /
• 2017

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

• Korean Management Science Review
• /
• v.29 no.3
• /
• pp.1-11
• /
• 2012

This paper considers a localization problem in time difference of arrival (TDOA)-based radio frequency identification (RFID) systems. To estimate the position of a target tag, this paper suggests three localization algorithms that use benchmark tags. The benchmark tags are the same type as the target tag, but either the locations or distance of the benchmark tags are known. Two algorithms use the benchmarks for auxiliary information to improve the estimation accuracy of the other localization algorithms such as least squared estimator (LSE). The other one utilizes the benchmarks as essential tags to estimate the location. Numerical tests show that the localization accuracy can be improved by using benchmark tags especially when an algorithm using the LSE is applied to the localization problem. Furthermore, this paper shows that our benchmark algorithm is valuable when the measurement noise is large.

• Progress in Medical Physics
• /
• v.7 no.1
• /
• pp.3-7
• /
• 1996

The accuracy in target localization of CT, MR, and digital angiography were investigated for stereotactic radiosurgery. The images using CT and MR were obtained out of geometrical phantom which was designed to produce exact coordinates of several points within a 0.lmm error range. The slice interval was 3mm and FOV was 35cm for CT and 28cm for MR. These images were transferred to treatment planning computer using TCP/IP in forms of GE format. Measured 3-D coordinates of these images from planning computer were compared to known values by geometrical phantom. Anterior-posterior and lateral films were taken by digital angiography for measurement of spatial accuracy. Target localization errors were 1.2${\pm}$0.5mm with CT images, 1.7${\pm}$0.4mm with MR-coronal images, and 2.1${\pm}$0.7mm with MR-sagittal images. But, in case of MR-axial images, the target localization error was 4.7${\pm}$0.9mm. Finally, the target localization error of digital angiography was 0.9${\pm}$0.4mm. The accuracy of diagnostic machines such as CT, MR, and angiography depended on their resolutions and distortions. The target localization error mainly depended on the resolution due to slice interval with CT and the image distortion as well as the resolution with MR However, in case of digital angiography, the target localization error was closely related to the distortion of fiducial markers. The results of our study should be considered when PTV (Planning Target Volume) was determined.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.28 no.10
• /
• pp.819-824
• /
• 2017

This paper presents a target localization scheme for distributed Multi-input Multi-output(MIMO) radar system using ToA measurements obtained from multiple transmitter and receiver pairs. The proposed method can locate the target from an arbitrary initial point by iteratively finding the Taylor linear approximation equation. The simulation results show that proposed method achieves the better mean square error(MSE) performance than the existing target localization methods, and furthermore, attains Cramer-Rao Lower Bound(CRLB).

• The KIPS Transactions:PartC
• /
• v.16C no.5
• /
• pp.645-652
• /
• 2009

Location-based service providing the customized information or service according to the user's location has attracted a lot of attention from the mobile communication industry. The service is realized by means of several building blocks, a localization scheme, service platform, application and service. The localization scheme figures out a moving target's position through measuring and processing a wireless signal. In this paper, we propose an adaptive localization scheme in an indoor localization system based on IEEE 802.15.4 standard. In order to enhance the localization accuracy, the proposed scheme selects the best reference points and adaptively reflects the changes of propagation environments of a moving target to approximate distances between the target and the reference points in RSS(Received Signal Strength) based localization system using triangulation. Through the implementation of the localization system, we verify the performance of the proposed scheme in terms of the localization accuracy.