Android application for indoor positioning of mobile devices using ultrasonic signals

Pérez, Mario; Gualda, David; Villadangos, J.M.; Ureña, J.; Pajuelo, P.; Diaz, E.; García, Enrique

doi:10.1109/ipin.2016.7743628

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…Some of the most recent works in the field of broadband ULPS are focused on the proposal of solutions for some of these problems, such as Doppler shift [ 25 ], multipath propagation [ 26 ] or Multiple Access Interference (MAI) [ 27 ]. A parallel trend is the development of ULPS for commercial portable devices, whose limited hardware resources and computing capability require a complete redesign of the signal processing algorithms [ 28 , 29 ]. The current state of evolution of these systems suggests their use for the accurate positioning of UAVs, a technological challenge that must face new problems that were not present in the positioning of previous receivers, such as the effect of the rotors on the propagation channel.…”

Section: Ultrasonic and Range Imaging Indoor Positioningmentioning

confidence: 99%

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

Paredes

Álvarez

Aguilera

et al. 2017

Sensors

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This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error.

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Section: Ultrasonic and Range Imaging Indoor Positioningmentioning

confidence: 99%

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

Paredes

Álvarez

Aguilera

et al. 2017

Sensors

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“…Pearson correlation coefficient [26] can be used to measure the similarity between the received and the reference signals. It is defined as ρ A, B = cov A, B σ A σ B (17) where A and B are the two signals, cov A, B is the covariance of A and B, σ A is the standard deviation of A and σ B is the standard deviation of B.…”

Section: Noise Filteringmentioning

confidence: 99%

Hybrid indoor location positioning system

Rashidzadeh

2019

IET wirel. sens. syst.

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Indoor location positioning techniques have experienced a significant growth in recent years. This work presents a hybrid indoor positioning system with fine and coarse modes. It utilises acoustic signals for fine positioning and received signal strength (RSS) for coarse location estimation. Acoustic positioning systems require a line-of-sight connection for accurate positioning which may not be available due to obstacles in indoor environments. A new solution is presented to overcome this problem using RSS as a reference to validate the line-of-sight connection. Moreover, a new digital signal processing algorithm using a matched filter is presented to enhance the system's robustness in indoor environments with low a signal-to-noise ratio. Experimental measurement results in an indoor environment show that the proposed solution can accurately determine indoor locations with <6 cm positioning error on average.

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“…Many VLCIPSs use photodiodes as receivers of signals from light‐emitting diodes (LEDs) and calculate positioning coordinates from the strengths of these signals –. Unfortunately, photodiodes are not frequently used and consumers cannot be required to use a photodiodes of receiver to configure store navigation devices.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] Many VLCIPSs use photodiodes as receivers of signals from light-emitting diodes (LEDs) and calculate positioning coordinates from the strengths of these signals. [9][10][11][12][13][14][15][16][17] Unfortunately, photodiodes are not frequently used and consumers cannot be required to use a photodiodes of receiver to configure store navigation devices. Since mobile devices are already considered to be personal necessities, in this work, the camera in a mobile device is used to receive VLC signals.…”

Section: Introductionmentioning

confidence: 99%

A high accuracy and efficiency indoor positioning system with visible light communication

Chen

2018

Trans Emerging Tel Tech

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Positioning technology is important to the development of location‐based services. This work proposes a highly accurate and efficient indoor positioning system that uses visible light communication technology. The system uses a fingerprinting positioning mechanism that is based on a mobile device. A dynamic k‐NN (DKNN) mechanism is developed to improve upon k‐nearest neighbor (k‐NN) positioning, which is based on fingerprinting. This work also proposes an integrated angle of arrival (AOA) and fingerprinting mechanism for three‐dimensional positioning, which reduces the time that would otherwise be required for fingerprinting training and improves the accuracy of the indoor positioning system. When used for fixed‐height two‐dimensional positioning, the mean error distance of the DKNN fingerprinting positioning mechanism is approximately 0.15 cm. The AOA mean error distance is approximately 11.43 cm. The traditional k‐NN mean error distance is approximately 1.47 cm. Ignoring the height error, the integrated AOA and fingerprinting positioning mechanisms have a mean error distance of approximately 2.43 cm; the traditional AOA mean error distance is approximately 11.10 cm, and the fingerprinting mean error distance is approximately 2.74 cm. When the height error is considered, the AOA and fingerprinting fusion positioning mechanisms have a mean error distance of approximately 2.83 cm; the AOA mean error distance is approximately 11.62 cm, and the fingerprinting mean error distance is approximately 4.15 cm. The experimental results therefore show that the proposed mechanism provides the highest positioning accuracy.

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Android application for indoor positioning of mobile devices using ultrasonic signals

Cited by 21 publications

References 13 publications

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras

Hybrid indoor location positioning system

A high accuracy and efficiency indoor positioning system with visible light communication

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