Localization of Static Remote Devices Using Smartphones

Pedro, Dário; Tomic, Slaviša; Bernardo, Luís; Beko, Marko; Oliveira, Rodolfo; Dinis, Rui; Pinto, Paulo

doi:10.1109/vtcspring.2018.8417726

Cited by 9 publications

(9 citation statements)

References 12 publications

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“…Several localization algorithms are analyzed using RSS samples collected by a smartphone in indoor scenarios, considering cooperative and non-cooperative scenarios. This paper extends [19], which analyzed an outdoor scenario using GNSS and a non-cooperative scenario only. The paper's main contributions include:…”

Section: Introductionmentioning

confidence: 81%

Algorithms for Estimating the Location of Remote Nodes Using Smartphones

Pedro

Tomic²,

Bernardo

et al. 2019

IEEE Access

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Locating the position of a remote node on a wireless network is becoming more relevant, as we move forward in the Internet of things and in autonomous vehicles. This paper proposes a new system to implement the location of remote nodes. A new prototype Android application has been developed to collect real measurements and to study the performance of several smartphone's sensors and location algorithms, including an innovative one, based on the second order cone programming (SOCP) relaxation. The application collects the WiFi access points information and the terminal location. An internal odometry module developed for the prototype is used when Android's service is unavailable. This paper compares the performance of existing location estimators given in closed form, an existing SOCP one, and the new SOCP location estimator proposed, which has reduced complexity. An algorithm to merge measurements from non-identical terminals is also proposed. Cooperative and terminal stand-alone operations are compared, showing a higher performance for SOCP-based ones, that are capable of estimating the path loss exponent and the transmission power. The heterogeneous terminals were also used in the tests. Our results show that the accurate positioning of static remote entities can be achieved using a single smartphone. On the other hand, the accurate real-time positioning of the mobile terminal is provided when three or more scattered terminal nodes cooperate sharing the samples taken synchronously.

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Section: Introductionmentioning

confidence: 81%

Algorithms for Estimating the Location of Remote Nodes Using Smartphones

Pedro

Tomic²,

Bernardo

et al. 2019

IEEE Access

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“…Furthermore, it might be of great practical interest to adapt the existing or develop novel estimators for a particular application, for instance for AAL [ 23 ] or smart indoor positioning systems for situation awareness in emergency situations [ 24 , 81 ]. AAL is a growing research area whose main goal is to create better life and healthcare conditions for older people and/or people with disabilities, by building a flexible set of basic and task-oriented services [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

On Target Localization Using Combined RSS and AoA Measurements

Tomic

Beko

Dinis

et al. 2018

Sensors

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This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results is presented. Finally, the main conclusions are summarized, and a set of future aspects and trends that might be interesting for future research in this area is identified.

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“…Georeferencing data requires information about GPS coordinates (latitude, longitude and altitude), high precision positioning [45,46], IMU attitude (yaw), camera lense's FOV, image's resolution and aspect ratio- Figure 18. The Parrot Bebop2 (Base_link) uses a counter-clockwise IMU sensor, which is in line with ROS REP:103 conventions [47] followed by the World and Map frames.…”

Section: Aerial Mappingmentioning

confidence: 99%

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV's mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV's downwash effect: Static textures-Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures-Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.Several methods for terrain classification have been proposed in different works. Texture algorithms, such as those proposed in [2][3][4][5], have been widely recommended to emphasize the high and low frequencies of the images, supporting image classification. Other algorithms use color information to classify terrains, such as presented in [6], which is able to distinguish four different terrain types within an image. During this process, each channel's pixel is divided by the square root of its own three channels intensity. The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16]. However, shallow water terrains increase the decision error due to laser reflection, which leads to a misclassification as non-water terrain.Although prior research work has proposed many good solutions for terrain classification, there is still a gap regarding the study of dynamic terrain. The previously mentioned algorithms suffer from a high sensitivity to changes in the environment, mainly due to changes in brightness, color and texture. Recent works [17,18] ...

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Localization of Static Remote Devices Using Smartphones

Cited by 9 publications

References 12 publications

Algorithms for Estimating the Location of Remote Nodes Using Smartphones

Algorithms for Estimating the Location of Remote Nodes Using Smartphones

On Target Localization Using Combined RSS and AoA Measurements

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

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