Beep: 3D indoor positioning using audible sound

Mandal, Atri; Lopes, Cristina V.; Givargis, Tony; Haghighat, A.; Jurdak, Raja; Baldi, Pierre

doi:10.1109/ccnc.2005.1405195

Cited by 91 publications

(59 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the accuracy of the positioning is poor, at a level not better than ±(1-2) m. Therefore, only the technologies suitable for the smartphone shown in Table 1 are compared and analyzed. The data from references (Mandal Al, 2005;Navizon, 2016;Dardari et al, 2012;Daly et al, 2010) are also summarized. To find the way to eliminate the drawbacks also a known system based on active magnetic positioning at local fields (Melzer and Moffit, 1997;Hansen, 1986;Blood, 1997) is also added to the consideration.…”

Section: The Literature Reviewmentioning

confidence: 99%

The active magnetic tracking with scalable coverage: indoor navigation for smartphones

Zhelamskij¹

2016

J. Sens. Sens. Syst.

View full text Add to dashboard Cite

Abstract. In this paper we explore how to use the magnetic tracking technique for indoor navigation. The six degrees of freedom (6 DoF) magnetic tracking technology is selected because it is less influenced by the environment and has the highest accuracy among other techniques. A new method is proposed, called the alternating direct current (ADC) method, which permits one to get a double update rate, reduce the influence of Earth's magnetic field by a factor of 2 and reduce the power consumption by a factor of 3. The theoretical basis of the new magnetic tracking method is given, including a mathematical modeling of the local field, a mathematical model of the measurement of the positioning field by a 3-D transducer based on the Hall effect sensors, and the mathematical model for the estimation of the measurement errors. A new, net-like source of the positioning local magnetic field is proposed for indoor navigation with a scalable coverage area. A mathematical model of the distributed positioning field is given. The results of simulations are shown.

show abstract

Section: The Literature Reviewmentioning

confidence: 99%

The active magnetic tracking with scalable coverage: indoor navigation for smartphones

Zhelamskij¹

2016

J. Sens. Sens. Syst.

View full text Add to dashboard Cite

show abstract

“…Audible Sound positioning system is a system where the position of a person or an object is determined in a building by using audible sound waves through sound cards of standard devices (Mautz, 2012;Fallah et al, 2013). Examples of such a system are Beep, BeepBeep and Guoguo as demonstrated in the studies of Mandal et al (2005), Peng, Shen, and Zhang (2012), K. Liu et al (2013) respectively. Other examples include the studies of Höflinger et al (2012), Rishabh et al (2012), Sertatil, Altinkaya, and Raoof (2012).…”

Section: Audible Sound Positioning Systemmentioning

confidence: 99%

“…The studies of Mandal et al (2005), Höflinger et al (2012), Rishabh et al (2012), K. Liu et al (2013) consist of mobile devices (e.g. mobile phone), acoustic receivers, central server and wireless network.…”

Section: Audible Sound Positioning Systemmentioning

confidence: 99%

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

147

129

View full text Add to dashboard Cite

The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

show abstract

“…When at least three Time-of-Flight measurements of the same mobile node are collected, a trilateration algorithm can be used to estimate node position [6].…”

Section: Position Fixingmentioning

confidence: 99%

Sensor network based localization for continuous tracking applications [invited paper]

Denegri

Zappatore

Davoli

2007

Proceedings of the ImmersCom

View full text Add to dashboard Cite

The increasing interest in systems able to provide users immersive services (e.g. domotics, context-aware applications, immersive distance learning tools) has encouraged the development of cheap and effective platforms aimed at tracking objects and people within a certain space. In this context, wireless sensor networks (WSNs) can play a very important role, since specialized sensors can be fruitfully exploited in order to generate/receive signals by means of which the WSN can derive the position of nodes jointed to the objects to be tracked.The paper presents an original localization platform that exploits a single-hop WSN, based on a Microchip MCU and a Cypress RF device, to tracking its moving nodes. Specifically, the nodes of the network are divided in three sets: the first set consists of anchor nodes that, accordingly to the commands from the sink (the central node of WSN), generate ultrasonic pulses. These ones are received by the second set of (moving) nodes, which estimate the pulse-triptime and communicate it to the sink. Finally, the last set is constituted by general purpose nodes that collect any kind of data from the surrounding field. The sink gathers all the data, computes the position of moving nodes and transfers information to external users on the Internet.The algorithms adopted to manage the network and to localize moving nodes are discussed. A working prototype based upon the hardware platform, software and protocol described in this paper has been deployed and tested, and some results are shown.

show abstract

Beep: 3D indoor positioning using audible sound

Cited by 91 publications

References 9 publications

The active magnetic tracking with scalable coverage: indoor navigation for smartphones

The active magnetic tracking with scalable coverage: indoor navigation for smartphones

A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sensor network based localization for continuous tracking applications [invited paper]

Contact Info

Product

Resources

About