• Title/Summary/Keyword: Target localization

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Real Time Indoor Localization Using Geomagnetic Fingerprinting and Pedestrian Dead Reckoning (지구 자기장 기반 지문인식 및 추측 항법을 결합한 실시간 실내 위치정보 서비스)

  • Jang, HoJun;Choi, Lynn
    • KIISE Transactions on Computing Practices
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    • v.23 no.4
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    • pp.210-216
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    • 2017
  • In the paper we propose and implement a new indoor localization system where the techniques of magnetic field based fingerprinting and pedestrian dead reckoning are combined. First, we determine a target's location by comparing acquired magnetic field values with a magnetic field map containing pre-collected field values at different locations and choosing the location having the closest value. As the target moves, we use pedestrian dead reckoning to estimate the expected moving path, reducing the maximum positioning error of the initial location. The system eliminates the problem of localization error accumulation in pedestrian dead reckoning with the help of the fingerprinting and does not require Wi-Fi AP infrastructure, enabling cost-effective localization solution.

Robust Relative Localization Using a Novel Modified Rounding Estimation Technique

  • Cho, Hyun-Jong;Kim, Won-Yeol;Joo, Yang-Ick;Seo, Dong-Hoan
    • Journal of Advanced Marine Engineering and Technology
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    • v.39 no.2
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    • pp.187-194
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    • 2015
  • Accurate relative location estimation is a key requirement in indoor localization systems based on wireless sensor networks (WSNs). However, although these systems have applied not only various optimization algorithms but also fusion with sensors to achieve high accuracy in position determination, they are difficult to provide accurate relative azimuth and locations to users because of cumulative errors in inertial sensors with time and the influence of external magnetic fields. This paper based on ultra-wideband positioning system, which is relatively suitable for indoor localization compared to other wireless communications, presents an indoor localization system for estimating relative azimuth and location of location-unaware nodes, referred to as target nodes without applying any algorithms with complex variable and constraints to achieve high accuracy. In the proposed method, the target nodes comprising three mobile nodes estimate the relative distance and azimuth from two reference nodes that can be installed by users. In addition, in the process of estimating the relative localization information acquired from the reference nodes, positioning errors are minimized through a novel modified rounding estimation technique in which Kalman filter is applied without any time consumption algorithms. Experimental results show the feasibility and validity of the proposed system.

Target Localization Method based on Extended Kalman Filter using Multipath Time Difference of Arrival (다중경로 도달시간차이를 이용한 확장칼만필터 기반의 표적 위치추정 기법)

  • Cho, Hyeon-Deok
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.6
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    • pp.251-257
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    • 2021
  • An underwater platform operating a passive sonar needs to acquire the target position to perform its mission. In an environment where sea-floor reflections exist, the position of a target can be estimated using the difference in the arrival time between the signals received through multipaths. In this paper, a method of localization for passive sonar is introduced, based on the EKF (Extended Kalman Filter) using the multipath time difference of arrival in underwater environments. TMA (Target Motion Analysis) requires accumulated measurements for long periods and has limitations on own-ship movement, allowing it to be used only in certain situations. The proposed method uses an EKF, which takes measurements of the time differences of the signal arrival in multipath environments. The method allows for target localization without restrictions on own-ship movement or the need for an observation time. To analyze the performance of the proposed method, simulation according to the distance and depth of the target was performed repeatedly, and the localization error according to the distance and water depth were analyzed. In addition, the correlation with the estimated position error was assessed by analyzing the arrival time difference according to the water depth.

A Three Dimensional Object Localization Scheme using A Smartphone (스마트폰을 이용한 물체의 3차원 위치 추정 기법)

  • Kwon, Oh-Heum;Joung, Myoung-Hwan;Song, Ha-Joo
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1200-1207
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    • 2017
  • Sensors in a smartphone can be used to measure various physical quantities. In this paper, we propose an object localization scheme in a three dimenstional using a smart phone. The proposed scheme estimates the location of an object by observing it from several different points. The direction to the target object and the locations of the observation points are collected at each observation point using the location sensor and the orientation sensor in the smartphone. Based on these observations, the proposed scheme derives three dimensional line of sight vectors and estimates the location of the target object that minimizes the estimation error. We implemented the proposed scheme on an Android smartphone and tested its performance by estimating the height of a building and characteristics of the proposed approach.

A Range-Free Localization Method using an RFID System -Applied to a Library Book Location System- (RFID 시스템을 이용한 거리 비종속 위치추정기법 -도서위치추정을 중심으로-)

  • Choi, Jung-Wook;Oh, Dong-Ik
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.11 no.2
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    • pp.559-569
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    • 2010
  • We propose an RFID system based on range-free localization method. This method recognizes pre-determined reference tags first, and then checks within which reference tag the target tag is placed closest. It estimates target tag's position relative to the reference tag's position. We use Aging Counter to estimate the distance between reference and target tags using the read ratio of RFID tags. Practicality of the proposed method is verified by applying it to a library book locating system.

Distributed Target Localization with Inaccurate Collaborative Sensors in Multipath Environments

  • Feng, Yuan;Yan, Qinsiwei;Tseng, Po-Hsuan;Hao, Ganlin;Wu, Nan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.5
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    • pp.2299-2318
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    • 2019
  • Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

Optimization of the Radial Basis Function Network Using Time-Frequency Localization (시간-주파수 분석을 이용한 방사 기준 함수 구조의 최적화)

  • 김성주;김용택;조현찬;전홍태
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2000.11a
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    • pp.459-462
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    • 2000
  • In this paper, we propose the initial optimized structure of the Radial Basis Function Network which is more simple in the part of the structure and converges more faster than Neural Network with the analysis method using Time-Frequency Localization. When we construct the hidden node with the Radial Basis Function whose localization is similar with an approximation target function in the plane of the Time and Frequency, we make a good decision of the initial structure having an ability of approximation.

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MRAL Post Processing based on LS for Performance Improvement of Active Sonar Localization (소나 위치 추정 성능 향상을 위한 LS기반 MRAL 후처리 기법)

  • Jang, Eun-Jeong;Han, Dong Seog
    • Journal of the Institute of Electronics and Information Engineers
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    • v.49 no.9
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    • pp.172-180
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    • 2012
  • In multi-static sonar for detecting an underwater target, received signals contain the target echo, reverberation and clutter. Clutter and reverberation are main causes of increasing the false alarm rate. MRAL classifies received signals according to the spatial similarity, and it regards classified signal as reflected signals from a reflector. MRAL reduces the false alarm rate this way. However, the results of MRAL can have localization errors. In this paper, an MRAL post processing algorithm is proposed to reduce the localization errors with the least square (LS) method.

Indoor Mobile Localization System and Stabilization of Localization Performance using Pre-filtering

  • Ko, Sang-Il;Choi, Jong-Suk;Kim, Byoung-Hoon
    • International Journal of Control, Automation, and Systems
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    • v.6 no.2
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    • pp.204-213
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    • 2008
  • In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.

Underwater Acoustic Research Trends with Machine Learning: Passive SONAR Applications

  • Yang, Haesang;Lee, Keunhwa;Choo, Youngmin;Kim, Kookhyun
    • Journal of Ocean Engineering and Technology
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    • v.34 no.3
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    • pp.227-236
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    • 2020
  • Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.