Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios

Maalek, Reza; Sadeghpour, Farnaz

doi:10.1016/j.autcon.2015.11.009

Cited by 49 publications

(45 citation statements)

References 22 publications

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“…In addition, the calculation of the accuracy of the estimated location of an object using the LiDAR sensor can be performed by other key performance indices. For example, the use of the Distance Root Mean Squared (DRMS) measure for the data that are tracked on the x-y plane (2D) and the Mean Radial Spherical Error (MRSE) measure for the data that are tracked in the x-y-z space (3D) were reported in [27,70]. Using derivable error formulas, any given random error and scan angle in the LiDAR range can be modelled and simulated.…”

Section: Sensor Reliability Assesmentmentioning

confidence: 99%

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Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

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Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

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Section: Sensor Reliability Assesmentmentioning

confidence: 99%

“…All of them were obtained from a simulation procedure. The data input consisted of geospatial statistics [70,96] that were extracted from the point cloud supplied by the LiDAR sensor, so that the models could generate the figures of merit in terms of accuracy error: DRMS and MRSE.…”

Section: Model Parametrization and Validationmentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

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“…were reported in [12,13]. Using derivable error formulas, any given random error and scan angle in the LiDAR range can be modelled and simulated.…”

Section: Sensor Reliability Assesmentmentioning

confidence: 99%

“…All of them were obtained from a simulation procedure. The data input consisted of geospatial statistics[13,43] which were extracted from the point cloud supplied by the LiDAR sensor, so that the models could generate the figures of merit in terms of accuracy error: DRMS and MRSE.The four AI-based strategies that were considered are as follows. First, a multilayer perceptron neural network with backpropagation (MLP) with two hidden layers, each with five neurons and sigmoid activation functions, and an output layer with a lineal activation function, two neurons, and 5000 epochs.…”

mentioning

confidence: 99%

Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System

Castaño¹,

Villalonga²,

Haber³

et al. 2019

Preprint

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Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

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“…Global positioning system (GPS) and BeiDou system (BDS) can provide location based service (LBS) outdoor, but can?t work indoor. Recently, various techniques have been proposed for indoor positioning system such as infrared (IR) [7], ultrasound [12], radio-frequency identification (RFID) [14], WiFi [15], Bluetooth [2], sensor networks [9], ultra-wideband (UWB) [6], geomagnetic [5] , vision analysis [11] and Pseudolite [13]. Each system takes advantage of a particular positioning technology or integrating some of these technologies.…”

mentioning

confidence: 99%

Error minimization with global optimization for difference of convex functions

Deng¹,

Hu²

2019

Discrete &Amp; Continuous Dynamical Systems - S

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In this paper, a hybrid positioning method based on global optimization for difference of convex functions (D.C.) with time of arrival (TOA) and angle of arrival (AOA) measurements are proposed. Traditional maximum likelihood (ML) formulation for indoor localization is a nonconvex optimization problem. The relaxation methods can?t provide a global solution. We establish a D.C. model for TOA/AOA fusion positioning model and give a solution with a global optimization. Simulations based on TC-OFDM signal system show that the proposed method is efficient and more robust as compared to the existing ML estimation and convex relaxation.

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Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios

Cited by 49 publications

References 22 publications

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System

Error minimization with global optimization for difference of convex functions

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