• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.177-182
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• 2012

We propose effective motion and observation models for the position of a WiFi-equipped smartphone user in large indoor environments. Three component motion models provide better proposal distribution of the pedestrian's motion. Our Gaussian interpolation-based observation model can generate likelihoods at locations for which no calibration data is available. These models being incorporated into the particle filter framework, our WiFi fingerprint-based localization algorithm can track the position of a smartphone user accurately in large indoor environments. Experiments carried with an Android smartphone in a multi-story building illustrate the performance of our WiFi localization algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2B
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• pp.99-106
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• 2009

The tracking information of objects or persons plays the important role for being advanced of the technology in the logistics management or processing. In this paper, we propose the TDOA-based localization method for port logistics environment for obtaining accurate location of a tag which is attached to some objects or persons. The proposed method consists of these modules for precision : An adaptive selection module of base reader, 2.5D locating method and position-restriction method using a map. This paper includes the performance evaluation of those methods. We could see the improved performance in the simulation. And we also implemented the system and achieved the accuracy of DRMS and CEP within 4 meters.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.331-339
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• 2024

Reliable and precise indoor localization is crucial for personal navigation, emergency rescue, and monitoring workers indoors. To use this technology in different applications, it is important to make it less dependent on infrastructure and to keep the error as small as possible. Fingerprinting stands out as a popular choice for indoor positioning because it leverages existing infrastructure and works with just a smartphone. However, its accuracy heavily relies on the quality of that infrastructure. For instance, having too few access points or beacons can greatly reduce its effectiveness. To reduce dependence on RF infrastructure, we have developed surface correlation (SC) using accumulated Received Signal Strength Indicator (RSSI) signals This approach constructs a user mask for radio map comparisons using an accumulated RSSI vector and the trajectory of the user, which is estimated through PDR. The location with the highest correlation is considered as the user's position after comparison. Through a simulation, the performance of short RSSI vector-based technology and SC is analyzed, and future directions for the development of SC are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1496-1521
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• 2012

Uncertainty is ubiquitous in target tracking wireless sensor networks due to environmental noise, randomness of target mobility and other factors. Sensing results are always unreliable. This paper considers unreliability as it occurs in wireless sensor networks and its impact on target-tracking accuracy. Firstly, we map intersection pairwise sensors' uncertain boundaries, which divides the monitor area into faces. Each face has a unique signature vector. For each target localization, a sampling vector is built after multiple grouping samplings determine whether the RSS (Received Signal Strength) for a pairwise nodes' is ordinal or flipped. A Fault-Tolerant Target-Tracking (FTTT) strategy is proposed, which transforms the tracking problem into a vector matching process that increases the tracking flexibility and accuracy while reducing the influence of in-the-filed factors. In addition, a heuristic matching algorithm is introduced to reduce the computational complexity. The fault tolerance of FTTT is also discussed. An extension of FTTT is then proposed by quantifying the pairwise uncertainty to further enhance robustness. Results show FTTT is more flexible, more robust and more accurate than parallel approaches.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2780-2782
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• 2005

In this paper, we present a mobile robot localization solution by using a Ubiquitous Vision System (UVS). The collective information gathered by multiple cameras that are strategically placed has many advantages. For example, aggregation of information from multiple viewpoints reduces the uncertainty about the robots' positions. We construct UVS as a multi-agent system by regarding each vision sensor as one vision agent (VA). Each VA performs target segmentation by color and motion information as well as visual tracking for multiple objects. Our modified identified contractnet (ICN) protocol is used for communication between VAs to coordinate multitask. This protocol raises scalability and modularity of thesystem because of independent number of VAs and needless calibration. Furthermore, the handover between VAs by using ICN is seamless. Experimental results show the robustness of the solution with respect to a widespread area. The performance in indoor environments shows the feasibility of the proposed solution in real-time.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.4
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• pp.33-40
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• 2005

This paper represents the stable path recognition by the ultrasonic sensor which gathers navigation environments and the monocular image sensor which generates the self localization information of mobile robot. The proposed ultrasonic sensor and vision camera system recognizes the target and extracts parameters for generating the world map and self localization. Therefore, this paper has developed an indoor mobile robot and has stably demonstrated in a corridor environment.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.907-908
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• 2006

We describe a method for localizing a mobile robot in its working environment using a vision system and Virtual Reality Modeling Language (VRML). The robot identifies landmarks in the environment, using image processing and neural network pattern matching techniques, and then its performs self-positioning with a vision system based on a well-known localization algorithm. After the self-positioning procedure, the 2-D scene of the vision is overlaid with the VRML scene. This paper describes how to realize the self-positioning, and shows the overlap between the 2-D and VRML scenes. The method successfully defines a robot's path.

• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.300-306
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• 2023

A hull-cleaning robot sticks to the surface of a vessel and moves for efficient cleaning. Precise path planning and tracking using the current position is crucial. Many robots rely on the INS algorithm, but errors accumulate. To fix this, GPS, sonar, and USBL are used, though with limitations. Selecting suitable sensors for the surface operation and accurate positioning algorithm are vital. In this study, we developed a robot position estimation algorithm using the structure of a ship. Problems that arise when expanding the 2D position estimation algorithm used in existing wall structures to 3D were evaluated and methods for solving them were proposed. In addition, we aimed to improve performance by deriving singularities that exist in the robot path and proposing an error correction algorithm based on the singularities.