Received signal strength based least squares lateration algorithm for indoor localization

Dağ, Tamer; Arsan, Taner

doi:10.1016/j.compeleceng.2017.08.014

Cited by 39 publications

(38 citation statements)

References 9 publications

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“…The ratio of the transmitted to the received power is used to estimate the position of the node. Indoor location tracking using RSS [22,23] employs three or more nodes to perform finegrained device tracking, similar to ToA [24]. Using the method of ranging, where the distance between two nodes is predefined, the coordinates of the device are computed using lateration or angulation approaches.…”

Section: Dolphin (Distributed Objectmentioning

confidence: 99%

“…The key idea of "mathematical programming" based approaches is to formulate the position estimation problem as a constraint optimization problem, which can be solved by iterative (e.g. [16-21, 32, 36, 42]) or direct methods (e.g., [23,25,28,31]). Direct methods are generally based on factorization of the matrix where this operation is absent in iterative methods.…”

Section: Comparative Studymentioning

confidence: 99%

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Mobile geolocation techniques for indoor environment monitoring

2020

KSII TIIS

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Advances in localization-based technologies and the increase in ubiquitous computing have led to a growing interest in location-based applications and services. High accuracy of the position of a wireless device is still a crucial requirement to be satisfied. Firstly, the rapid development of wireless communication technologies has affected the location accuracy of radio monitoring systems employed locally and globally. Secondly, the location is determined using standard complex computing methods and needs a relatively long execution time. In this paper, two geolocalization techniques, based on trigonometric and CORDIC computing processes, are proposed and implemented for Bluetooth-based indoor monitoring applications. Theoretical analysis and simulation results are investigated in terms of accuracy, scalability, and responsiveness. They show that the proposed techniques can locate a target wireless device accurately and are well suited for timing estimation.

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Section: Dolphin (Distributed Objectmentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

Mobile geolocation techniques for indoor environment monitoring

2020

KSII TIIS

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“…One possible technique is triangulation [16] but this requires knowledge of the angle from the drone to the user. In comparison we have applied trilateration [17] as this technique is able to locate a target by using the distance information between the target and several reference points.…”

Section: Related Workmentioning

confidence: 99%

“…The drone AP is able to localise in 150s and maintain strong RSS for the smartphone It is then a matter of applying equation 4[17] and equation 5 to all devices:…”

mentioning

confidence: 99%

Autonomous Flying WiFi Access Point

Nunns

Chen

Chang

et al. 2019

2019 IEEE Symposium on Computers and Communications (ISCC)

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Unmanned aerial vehicles (UAVs), aka drones, are widely used civil and commercial applications. A promising one is to use the drones as relying nodes to extend the wireless coverage. However, existing solutions only focus on deploying them to predefined locations. After that, they either remain stationary or only move in predefined trajectories throughout the whole deployment. In the open outdoor scenarios such as search and rescue or large music events, etc., users can move and cluster dynamically. As a result, network demand will change constantly over time and hence will require the drones to adapt dynamically.In this paper, we present a proof of concept implementation of an UAV access point (AP) which can dynamically reposition itself depends on the users movement on the ground. Our solution is to continuously keeping track of the received signal strength from the user devices for estimating the distance between users devices and the drone, followed by trilateration to localise them. This process is challenging because our on-site measurements show that the heterogeneity of user devices means that change of their signal strengths reacts very differently to the change of distance to the drone AP. Our initial results demonstrate that our drone is able to effectively localise users and autonomously moving to a position closer to them.Index Terms-UAV access point, Flying access point, Drone based user localisation

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“…The lateration derivations estimate the location of an object by measuring its distances from more than one reference point. Received signal strengths (RSS) [20][21][22], the time of arrival (ToA) or time difference of arrival (TDoA) methods are usually used [23], instead of measuring the distance directly. In these methods, distance is calculated by computing the attenuation of the emitted signal strength or by multiplying the travel time and radio signal velocity.…”

Section: Introductionmentioning

confidence: 99%

Accurate indoor positioning with ultra-wide band sensors

Arsan¹

2020

Turk J Elec Eng & Comp Sci

Self Cite

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Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m and 50 cm × 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm . Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm . Key words: Indoor positioning, ultra-wide band sensors, optimization, big bang-big crunch algorithm, genetic algorithms, the Kalman filter (A-GPS) technology is a navigation system used on mobile devices that helps to identify a user's location via the base station's A-GPS address server [3]. The accuracy provided by the GPS in determining the outdoor location is between 3 m and 15 m , usually around 10 m . The accuracy that A-GPS provides in determining the outdoor location is about 15 m, while the interior is 50 m [3]. As can be seen from the accuracy values, these solutions are sufficient to determine the outdoor position, in other words, the position in the open space.On the other hand, given the fact that people now spend more than 80% of their time indoors, a suitable system for locating interior space is needed. Unfortunately, the use of GPS satellites in indoor location determination is not possible as GPS signals are weaken due to atmospheric delays, multi-paths, steel structures, roofs and building walls [1][2][3]. For this reason, efforts have been made to develop new technologies that allow reliable indoor location determination with high accuracy, low average positioning error over the past two decades.

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Received signal strength based least squares lateration algorithm for indoor localization

Cited by 39 publications

References 9 publications

Mobile geolocation techniques for indoor environment monitoring

Mobile geolocation techniques for indoor environment monitoring

Autonomous Flying WiFi Access Point

Accurate indoor positioning with ultra-wide band sensors

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