Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object

Vadakkepat, Prahlad; Liu, Jing

doi:10.1109/tim.2006.881569

Cited by 59 publications

(28 citation statements)

References 13 publications

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“…그리고 유선 항법 시 스템은 매설된 유도선을 인식하기 위한 장치와 바닥과의 거 리가 매우 가깝기 때문에 서스펜션에 의해 장치가 파손될 가능성이 매우 높다 [7][8][9][10]. DGPS 또는 카메라 등을 이용한 시각 센서 기반 주행 시스템과 같은 실외에서도 사용이 가 능한 항법 시스템이 존재하지만 이는, 오차 범위가 매우 크 거나, 연산이 매우 복잡하다는 단점으로 인해 안전성이 크 게 요구되는 산업 현장의 무인운반차에 적용하기에는 비효 율적이다 [11][12][13].…”

Section: 서 론unclassified

Development of Magnet Position Device for Outdoor Magnet Guidance Vehicle

Cho¹,

Kim²

2014

Journal of Korean Institute of Intelligent Systems

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This paper is research paper on the MPD(Magnet Position Device) for the outdoor MGV(Magnet /Magnet Gyro Guidance Vehicle). Usually, MGV is used in indoor environment because of a measurement height of the magnet position device. CMPD(Commercial magnet position device) has 30 mm measurement height, so this is suitable structure in indoor environment like to a flat surface. Outdoor environment is an uneven and irregular, So Outdoor MGV must has a suspension. But CMPD is unsuitable for outdoor environment because of a collision with a surface caused by suspension. Thus, measurement height of the outdoor MPD is positively necessary more than 100 mm. So, we suggest the outdoor MPD using analog magnet hall sensor, moving average filter and Characteristic(rate of the magnet hall sensor) function of the localization. Result of the experiments, the proposed Magnet Position Device for the outdoor MGV has localization accuracy 4.31 mm, measurement height 150 mm and width 150 mm and is efficient more than CMPD.

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Section: 서 론unclassified

Development of Magnet Position Device for Outdoor Magnet Guidance Vehicle

Cho¹,

Kim²

2014

Journal of Korean Institute of Intelligent Systems

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“…In the field of instrumentation and measurement several mobility sensors types have been used-accelerometers [4], [5], cameras [2], [3], and RFID tags [7]. Hache et al [4] distinguished mobile from stationary states with 97.4% accuracy by applying thresholds on sensor data.…”

Section: A Related Work In Motion Detection and Rfidmentioning

confidence: 99%

“…Accelerometers are reliable and precise, but our domain requires either low-cost sensors or 0018-9456 c 2013 IEEE cameras to track a large number of small, inexpensive, and disposable objects. Cameras provide rich contextual information and statistical methods for motion tracking [2], [3], but raise privacy concerns, especially in medical domains. Hence, we opted for passive RFID.…”

Section: A Related Work In Motion Detection and Rfidmentioning

confidence: 99%

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Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

Parlak

Marsic

2013

IEEE Trans. Instrum. Meas.

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Abstract-We studied object motion detection in an indoor environment using RFID technology. Unlike prior work, we focus on dynamic scenarios, such as emergency medical situations, subject to signal interference by people and many RFID tags. We build a realistic trauma resuscitation setting and record a dataset of around 14 000 detection instances. We find that factors affecting radio signal, such as tag motion, have different statistical fingerprints, making them discernible using statistical methods. Our method for object motion detection extracts descriptive features of the received signal strength and classifies them using machine-learning techniques. We report experimental results obtained with several statistical features and classifiers, and provide guidelines for feature and classifier selection in different environments. Experimental results show that object motion could be detected with an accuracy of 80% in complex scenarios and 90% on average. The motion type, on the other hand, could not be identified with such high accuracy using currently available passive RFID technology.

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“…Some examples of inertial-based mobile IPS include mobile asset navigation, mobile first-responder navigation and mobile emergency rescue and tracking [20][21][22][23].One of the major issue of inertial-based mobile IPS, is sample impoverishment during particle filtering. This phenomenon will cause computation burden to the overall systems [24]. Two (2) reason behind this issue, whether it caused by the of small noise measurement in accurate sensor or small number of particle [25].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of the RSIL to LLVM IR Translator for Verification of the Intermediate Code on IoT Virtual Machine

Son¹,

Oh²,

Lee³

2017

IJCA

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The phenomenon of sample impoverishment during particle filtering always contribute computation burden to the inertial-based mobile IPS systems. This is due to the factor of noise measurement and number of particle. Usually, the special strategies resampling algorithms was used. However, these algorithms just can fit in certain environment. This needs an adaptation of noise measurement and number of particle in a algorithm in order to make resampling with more intelligence, reliability and robust. In tbis paper, we will propose an adaptive special strategies resampling by adapting noise and particle measurement. These adaptation is used to determine the most suitable algorithm of special strategies resampling, that can be switched for resampling purpose. Finally, we illustrate our proposed solution our for indoor environment setup.

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Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object

Cited by 59 publications

References 13 publications

Development of Magnet Position Device for Outdoor Magnet Guidance Vehicle

Development of Magnet Position Device for Outdoor Magnet Guidance Vehicle

Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

Design and Implementation of the RSIL to LLVM IR Translator for Verification of the Intermediate Code on IoT Virtual Machine

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