Performance evaluation of ultra wideband technology for construction resource location tracking in harsh environments

Cheng, Tao; Venugopal, Manu; Teizer, Jochen; Vela, Patricio A.

doi:10.1016/j.autcon.2011.05.001

Cited by 219 publications

(128 citation statements)

References 23 publications

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“…Most of the studies reported in the literature investigated situations that involved direct line of sight and sometimes partial occlusions (Cheng, Venugopal, Teizer, & Vela, 2011;Cho, Youn, & Martinez, 2010;Gu & Taylor, 2003;Teizer et al, 2008). Cho et al (2010) investigated the overall performance of a UWB system in steel versus wood-framed construction environments, although complete occlusion of the tags (i.e., lack of line-of-sight) was not considered.…”

Section: Gps and Uwbmentioning

confidence: 99%

“…Cho et al (2010) investigated the overall performance of a UWB system in steel versus wood-framed construction environments, although complete occlusion of the tags (i.e., lack of line-of-sight) was not considered. Cheng et al (2011) investigated the performance of a UWB system in real construction projects for safety and productivity purposes; however, the study area did not include significant occlusions or obstructions, and remained static during the study period.…”

Section: Gps and Uwbmentioning

confidence: 99%

See 1 more Smart Citation

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

Shahi

Cardona²,

Haas³

et al. 2012

Construction Research Congress 2012

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Section: Gps and Uwbmentioning

confidence: 99%

Section: Gps and Uwbmentioning

confidence: 99%

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

Shahi

Cardona²,

Haas³

et al. 2012

Construction Research Congress 2012

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“…The proximity technique requires the measurement of a set of neighboring reference points near by the target object, which are fixed and have known coordination [17]. Thus, the efficient proximity algorithm ensures the rapid and precise search of neighboring points.…”

Section: B Proximity Techniquementioning

confidence: 99%

A Low-cost RFID/GPS Location Sensing Algorithm for Urban Infrastructure

Jing¹,

Mingyi²,

Shang³

2015

Int. J. Onl. Eng.

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A Ab bs st tr ra ac ct t-Low-cost Radio Frequency Identification (RFID) location sensing algorithm is practical and crucial for urban infrastructure location. Current RFID location algorithms are based on fixed multiple RFID reader or reference points (reader or tag) with known position, which is cost and difficult to deployment in urban environment. The objective of this study is to develop a low-cost location sensing algorithm using RFID technique to locate urban infrastructure. This paper presents an algorithm based on multilateration with the integration of mobile RFID and Global Position System (GPS). The mobility of RFID reader can measure many distances between different RFID reader positions and target tag which represent multilateration. The GPS provides coordination of RFID reader. The location of tag is calculated based on triangle geometric property. The redundant observation is adopted to get the optimum location. In practice, the only one RFID reader is necessary, thus the cost is reduced hardly. The performance and accuracy assessment are conducted in field experiment. The experiment proves that the proposed low-cost location sensing algorithm is able to location the urban infrastructure with smaller position error. The lower cost makes it even more economically feasible than other algorithm.

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“…They work based on the time-of-arrival principle; specifically, that the propagation time of a signal can be translated directly into distance from the source to the receiver [11]. These sensors are attached to equipment or workers and provide their 3D location in real-time.…”

Section: Activity Recognition Of Construction Equipment Using Active mentioning

confidence: 99%

“…This makes the problem more complex. The choice of a particular sensor depends on several factors including the application type, line-of-sight between objects and sensors, required signal strength, calibration requirements, permitted bandwidth, implementation costs, and environmental conditions [11].…”

Section: Activity Recognition Of Construction Equipment Using Active mentioning

confidence: 99%

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

Cheng¹,

Rashidi²,

Davenport³

et al. 2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Action recognition and tracking of construction heavy equipment is the first step for benchmarking and analyzing the performance of individual machines and evaluating the productivity of a jobsite as a whole. Aside from direct observations, the current approaches for automatically recognizing and tracking various actions of construction heavy equipment includes: 1) using active sensors such as RFID tags, GPS and accelerometers or 2) computer vision-based activity analysis (processing images or videos).In this paper, we present a novel audio-based approach for activity recognition of construction heavy equipment. Construction machines often produce distinct sound patterns while performing certain activities and it is possible to extract useful information by recording and processing those audio files at construction jobsites. The proposed audiobased framework begins with recording generated sound patterns of construction equipment using commercially available audio recorders. The recorded signal is then fed into a signal enhancement algorithm to reduce background noise commonly found at construction jobsites. The modified audio signal is then converted into a time-frequency representation using the Short-Time Fourier Transform (STFT). A Support Vector Machine (SVM) is then trained to differentiate between the acoustic patterns of the various activities of each machine. The processed audio signal is then finally divided and classified into various activities using a window filtering approach and by setting proper thresholds. We implemented the presented audio-based system at several jobsites as case studies and the results illustrate the efficiency of the system in automatically recognizing various actions of construction heavy equipment.

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Performance evaluation of ultra wideband technology for construction resource location tracking in harsh environments

Cited by 219 publications

References 23 publications

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

A Low-cost RFID/GPS Location Sensing Algorithm for Urban Infrastructure

Audio Signal Processing for Activity Recognition of Construction Heavy Equipment

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