Indoor Localization With Range-Based Measurements and Little Prior Information

Arias-de-Reyna, Eva; Djurić, Petar M.

doi:10.1109/jsen.2013.2246359

Cited by 20 publications

(12 citation statements)

References 17 publications

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“…Another study yielded satisfactory results in simulation experiments. The study of Arias‐de‐Reyna and Djurić suggested taking three beacons, collecting data on signal strength and message time, and then running triangle localization to analyze current position of the user. The simulation results of the work of the aforementioned authors showed smaller positioning error compared to that of other methods.…”

Section: Related Workmentioning

confidence: 99%

“…The study of Arias‐de‐Reyna and Djurić suggested taking three beacons, collecting data on signal strength and message time, and then running triangle localization to analyze current position of the user. The simulation results of the work of the aforementioned authors showed smaller positioning error compared to that of other methods. The later study proposed using RFID to detect activities in elderly people; it suggests attaching RFID tags onto clothing of the elderly people, having them carry RFID readers, installing UHF RFID readers indoors, and collecting different positions of the elderly people and the received signal strength of RFID during different activities; the collected data is then run through support vector machines for learning and categorizing signals and behaviors, so that when a mobile phone registers RFID received signal strength, it can determine current position and behavior of the elderly people.…”

Section: Related Workmentioning

confidence: 99%

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A home security system for seniors based on the beacon technology

Tsai

2018

Concurrency and Computation

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Summary Many elderly people have a hard time adapting to information technology (IT); for instance, many wearable devices have such rich functions that render them highly power‐consuming, creating operational difficulties for the elderly. The wearable devices must constantly be recharged, which is also an issue for disabled elderly people. Hence, this study utilizes transmitter and receiver of the beacon to learn about the habits of elderly people in their residence while monitoring their heart rate to track their physical condition; when and if they press the medical alarm, help can be sent on their way. This study uses the beacon device to position the location of elderly people in their residence. We have developed a wearable wristband that transmits data through the beacon. The only button on the wristband is a medical alarm; other functions include home positioning and heart rate monitoring, which operate automatically to relieve the elderly people from information technology adaptation issues. This paper will also develop an integrative monitoring platform that aims at detecting whether an elderly person has pressed the medical alarm as well as analyzing whether their heart rate is regular. Given that elderly people stay mostly within the range of their residence, the platform will conduct statistical analysis on schedule of the elderly person of being in and out of the house; if they have left their house for too long or have displayed irregular behavior in leaving or entering the house, then their family will be notified. The proposed method has added privacy and access authority control to prevent malicious parties from tracking the schedule or the residence of elderly people from being invaded. This study has also completed application development and conducted privacy efficacy analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A home security system for seniors based on the beacon technology

Tsai

2018

Concurrency and Computation

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“…Additionally, several literature works have proposed many algorithms that either use pre-assumptions or estimate the parameters to obtain the NLOS information. The NLOS-related methods are beyond the scope of this paper; please refer to other research works in [ 20 , 23 , 29 ].…”

Section: Simulationmentioning

confidence: 99%

“…A linear CRLB (L-CRLB) is proposed, which considers the linearized effect and provided the lower bound for such an estimator [ 19 ]. Other similar works also give CRLB for different ranging techniques [ 20 , 21 ]. Although some other methods can be used for performance analysis [ 22 ], CRLB is still popular for wireless localization researchers due to its simplicity and general expression.…”

Section: Introductionmentioning

confidence: 99%

Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks

Zhao

Zhang

et al. 2016

Sensors

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In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér–Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for the indoor localization systems and serves as an indicator for practical system evaluation.

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“…Self-localization capability is necessary for WSN sensor nodes, especially when they are deployed randomly or moved after deployment. To reduce the implementation cost and energy consumption, special sensor nodes, called anchor nodes, are installed with known positions [4][5][6][7][8][9][10][11] to localize a group of sensor nodes in the vicinity. The simplest way to determine the positions of anchor nodes is to use Global Positioning System (GPS) devices [4,[12][13][14][15][16][17][18] or is to be manually measured [8,10,11] in advance.…”

Section: Introductionmentioning

confidence: 99%

Locating Anchors in WSN Using a Wearable IMU-Camera

Huang

Tseng

2014

International Journal of Distributed Sensor Networks

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Localization in a wireless sensor network (WSN) becomes important for many modern applications, like landslide detection, precision agriculture, health care, and so forth. The more precise the position of an anchor node is, the more accurate the localization of a sensor node can be measured. Since the Global Positioning System (GPS) device cannot work properly indoor, some existing localization methods configure anchor nodes in a manual fashion. However, neither applying GPS modules nor manually configuring anchor nodes is suitable for a WSN and especially artificial errors of manual configuration may be propagated and affect the results of localization. In this paper, we propose an alternative method to estimate anchor node locations in an indoor environment. We collect the Received Signal Strength Indicator (RSSI) data from the anchor node when human is walking around them. Meanwhile, we use a wearable IMU-camera device to assist the moving trajectory estimation. We implement a monocular Visual Odometry with a human walking model to estimate moving trajectories. An Unscented Kalman Filter (UKF) is used to estimate the anchor node location by fusing the RSSI data and moving trajectory. The experiment results show that the proposed method has lower estimation error when locating anchors.

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Indoor Localization With Range-Based Measurements and Little Prior Information

Cited by 20 publications

References 17 publications

A home security system for seniors based on the beacon technology

A home security system for seniors based on the beacon technology

Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks

Locating Anchors in WSN Using a Wearable IMU-Camera

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