Smarttag: An Indoor Positioning System Based on Smart Transmit Power Scheme Using Active Tags

Şahin, Sühap; Özcan, Hikmetcan; Küçük, Kerem

doi:10.1109/access.2018.2824538

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…The localization system is based on fingerprinting framework, since it allows the use of simple Received Signal Strength (RSS) measurements, which does not require any additional modification of devices. Fingerprinting approach has a significant advantage when compared to RSS based trilateration [ 12 ] since information about transmit power is not required. Moreover, the effect of shadowing caused by walls and other obstacles as well as multipath fluctuation does not have to be considered when fingerprinting-based localization is implemented.…”

Section: Introductionmentioning

confidence: 99%

“…One way to avoid time-consuming calibration measurements for radio map is to use complex simulation of radio signal propagation, based either on signal modelling or raytracing algorithms [14]. However, these complex simulations still cannot provide realistic estimates of RSS levels in a dynamic indoor environment and can reduce localization performance [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Algorithm for Dynamic Fingerprinting Radio Map Creation Using IMU Measurements

Brída

Machaj

Racko

et al. 2021

Sensors

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While a vast number of location-based services appeared lately, indoor positioning solutions are developed to provide reliable position information in environments where traditionally used satellite-based positioning systems cannot provide access to accurate position estimates. Indoor positioning systems can be based on many technologies; however, radio networks and more precisely Wi-Fi networks seem to attract the attention of a majority of the research teams. The most widely used localization approach used in Wi-Fi-based systems is based on fingerprinting framework. Fingerprinting algorithms, however, require a radio map for position estimation. This paper will describe a solution for dynamic radio map creation, which is aimed to reduce the time required to build a radio map. The proposed solution is using measurements from IMUs (Inertial Measurement Units), which are processed with a particle filter dead reckoning algorithm. Reference points (RPs) generated by the implemented dead reckoning algorithm are then processed by the proposed reference point merging algorithm, in order to optimize the radio map size and merge similar RPs. The proposed solution was tested in a real-world environment and evaluated by the implementation of deterministic fingerprinting positioning algorithms, and the achieved results were compared with results achieved with a static radio map. The achieved results presented in the paper show that positioning algorithms achieved similar accuracy even with a dynamic map with a low density of reference points.

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Section: Introductionmentioning

confidence: 99%