Beacon Distance Measurement Method in Indoor Ubiquitous Computing Environment

Sung, Yunsick; Kwak, Jeonghoon; Jeong, Young‐Sik; Park, Jong Hyuk

doi:10.1007/978-981-10-0068-3_15

Cited by 7 publications

(9 citation statements)

References 3 publications

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“…Figure 6 shows the distances estimated by the proposed method. The lowest and highest RSSIs were found to correspond to distances of 1 and 2 m, respectively, using the relative distance estimation method [7]. The sudden increases in the signals were also reduced accordingly.…”

Section: Performance Comparisonmentioning

confidence: 89%

“…The Figure 11. Comparison of the distance estimation results of the proposed method and ReDEM [7]. To analyze the accuracy of the proposed method, its results were compared with those of a traditional ReDEM.…”

Section: Discussionmentioning

confidence: 99%

“…The model was used to predict the signal path in such a way that the RSSIs varied linearly with the distance. The log-distance path loss model has been applied to RSSIs in another study [7], and also to the measurement of the distance from an AP [8]. There is a limit to improving the accuracy of the revised RSSIs based on the information of measured RSSIs.…”

Section: Rssi-related Workmentioning

confidence: 99%

“…For example, environmental information such as locations, configurations, and so on can be utilized. For distance estimation, the beacon signal-based distance is estimated based on the minimum and maximum signals [7]. Given the irregularity of RSSIs measured in indoor environments, any observed changes in the RSSIs should be taken into consideration.…”

Section: Rssi-related Workmentioning

confidence: 99%

“…There is thus the need for further revision of the distance to increase the accuracy. In this paper, the Kalman-filtered distances were normalized using the relative distance estimation method [7]. Using the maximum and minimum distances determined in the offline phase, the relative distance r" t is estimated based on the RSSI r t in the online phase.…”

Section: Relative Distance Estimation Stagementioning

confidence: 99%

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RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments

Sung

2016

Sustainability

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Given that location information is the key to providing a variety of services in sustainable indoor computing environments, it is required to obtain accurate locations. Locations can be estimated by three distances from three fixed points. Therefore, if the distance between two points can be measured or estimated accurately, the location in indoor environments can be estimated. To increase the accuracy of the measured distance, noise filtering, signal revision, and distance estimation processes are generally performed. This paper proposes a novel framework for estimating the distance between a beacon and an access point (AP) in a sustainable indoor computing environment. Diverse types of received strength signal indications (RSSIs) are used for WiFi, Bluetooth, and radio signals, and the proposed distance estimation framework is unique in that it is independent of the specific wireless signal involved, being based on the Bluetooth signal of the beacon. Generally, RSSI measurement, noise filtering, and revision are required for distance estimation using RSSIs. The employed RSSIs are first measured from an AP, with multiple APs sometimes used to increase the accuracy of the distance estimation. Owing to the inevitable presence of noise in the measured RSSIs, the application of noise filtering is essential, and further revision is used to address the inaccuracy and instability that characterizes RSSIs measured in an indoor environment. The revised RSSIs are then used to estimate the distance. The proposed distance estimation framework uses one AP to measure the RSSIs, a Kalman filter to eliminate noise, and a log-distance path loss model to revise the measured RSSIs. In the experimental implementation of the framework, both a RSSI filter and a Kalman filter were respectively used for noise elimination to comparatively evaluate the performance of the latter for the specific application. The Kalman filter was found to reduce the accumulated errors by 8% relative to the RSSI filter. This confirmed the accuracy of the proposed distance estimation framework.

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Section: Performance Comparisonmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Rssi-related Workmentioning

confidence: 99%

Section: Rssi-related Workmentioning

confidence: 99%

Section: Relative Distance Estimation Stagementioning

confidence: 99%

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RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments

Sung

2016

Sustainability

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An integrated solution for an autonomous drone racing in indoor environments

Rezende

Miranda

Rezeck

et al. 2021

Intel Serv Robotics

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The use of drones is becoming more present in modern daily life. One of the most challenging tasks associated with these vehicles is the development of perception and autonomous navigation systems. Competitions such as Artificial Intelligence Robotic Racing (AIRR) and Autonomous Drone Racing were launched to drive the advances in such strategies, requiring the development of integrated systems for autonomous navigation in a racing environment. In this context, this paper presents an improved integrated solution for autonomous drone racing, which focuses on simple, robust, and computationally efficient techniques to enable the application in embedded hardware. The strategy is divided into four modules: (i) A trajectory planner computes a path that passes through the sequence of desired gates; (ii) a perception system that obtains the global pose of the vehicle by using an onboard camera; (iii) a localization system which merges several sensed information to estimate the drone's states; and, (iv) an artificial vector field-based controller in charge of following the plan by using the estimated states. To evaluate the performance of the entire integrated system, we extended the FlightGoggles simulator to use gates similar to those used in the AIRR competition. A computational cost analysis demonstrates the high performance of the proposed perception method running on limited hardware commonly embedded in aerial robots. In addition, we evaluate the localization system by comparing it with ground truth and when there is a disturbance in the location of the gates. Results in a representative race circuit based on the AIRR competition showed the proposed strategy's competing performance, presenting itself as a feasible solution for drone racing systems.

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Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Kang

Cha

2018

Computer aided Civil Eng

281

144

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Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring are costly and inefficient because of the number of inspectors and sensors required. To date, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has become popular, but UAVs require skilled pilots or a global positioning system (GPS) for autonomous flight. Unfortunately, GPS cannot be used by a UAV for autonomous flight near some parts of certain structures (e.g., beneath a bridge), but these are the critical locations that should be inspected to monitor and maintain structural health. To address this difficulty, this article proposes an autonomous UAV method using ultrasonic beacons to replace the role of GPS, a deep convolutional neural network (CNN) for damage detection, and a geo‐tagging method for the localization of damage. Concrete cracks, as an example of structural damage, were successfully detected with 97.7% specificity and 91.9% sensitivity, by processing video data collected from an autonomous UAV.

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Beacon Distance Measurement Method in Indoor Ubiquitous Computing Environment

Cited by 7 publications

References 3 publications

RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments

RSSI-Based Distance Estimation Framework Using a Kalman Filter for Sustainable Indoor Computing Environments

An integrated solution for an autonomous drone racing in indoor environments

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

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