A novel ultrasonic indoor localization system with simultaneous estimation of position and velocity

Scherhäufl, Martin; Pfeil, Reimar; Pichler, Markus; Berger, Achim

doi:10.1109/wisnet.2012.6172145

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…Multiple technologies have been applied in the field of indoor localization. For instance, there are systems based on radio frequency identification [19,51], ultra-wideband transmission [21,44], Bluetooth [3,8], ultrasonic [36,39] and video signals [29]. However, these methods require the installation of anchors in indoor spaces to communicate with users' devices or generate specific signal distributions.…”

Section: Related Workmentioning

confidence: 99%

“…Indoor tracking and localization have been popular research areas. Most of the previous approaches are based on wireless anchors [3,8,18,21,30,36,37,39,44,50]. For instance, Wi-Fi access points generate specific signal strength distributions in indoor space, and mobile devices such as cellphones measure the signal strength to locate themselves [2,5,14,15,33,49].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ShoesLoc

Jin

Nahrstedt

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Currently, in-shoe force sensors have been widely used for step counting and gait analysis. However, it has not been realized that in-shoe force sensors are also capable of tracking walking paths. In this paper, we present ShoesLoc, an indoor walking path tracking method based on in-shoe force sensors. We show that, based on the force signals from a user's shoes, it is possible to estimate the walking direction change and the stride length of each step with machine learning techniques. We further apply a particle filter to combine this information with the constraint of barriers in floor maps, and thus can determine the walking path and the current position of the user. To solve the problem of the low accuracy caused by cumulative walking direction errors, we improve the particle filter by designing the direction correction algorithm. Moreover, we propose the weight normalization method to handle the impact of handbags and backpacks. Our experimental results show that, after a convergence phase, ShoesLoc achieves the average location error of 0.9-1.3 m. Compared with traditional indoor tracking technologies, ShoesLoc does not require the installation of wireless anchors, and has good robustness to environment changes such as the magnetic interference. CCS Concepts: • Information systems → Location based services; • Human-centered computing → Ubiquitous and mobile computing systems and tools.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ShoesLoc

Jin

Nahrstedt

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…There are multiple technologies that have been applied in this field. For instance, there are indoor localization systems based on RFID [16,17], UWB [19,36], Bluetooth [1,6] and ultrasonic [32,34]. Among the typical indoor localization technologies, we are mostly interested in the WLAN positioning systems using RSSI fingerprints.…”

Section: Related Workmentioning

confidence: 99%

Mmloc

Ren

Nahrstedt

2019

Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Indoor localization based on Wi-Fi fingerprints has been an active research topic for years. However, existing approaches do not consider the instability of access points (APs) which may be unreliable in practice, particularly the ones deployed by individual users. This instability impacts the localization accuracy severely, due to the unreliable or even wrong Wi-Fi fingerprints. Ideally, the localization should be done using only the well-deployed APs (e.g., deployed by facility teams). However, in many places the number of these APs is too few to achieve a good localization accuracy. To solve this problem, we leverage emerging smart APs equipped with multimode antennas, and build a new indoor localization system called MMLOC to reduce the number of necessary APs. The key idea is controlling the modes of AP antennas to generate more fingerprints with fewer APs. A clustering based localization strategy is designed to enable a mobile terminal to figure out the RSSI (Received Signal Strength Indicator) for different antenna modes without requiring any synchronization. We have implemented a prototype system using smart APs and commercial smartphones. Experimental results demonstrate that MMLOC can reduce the number of necessary APs by 50%, and achieve the same or even better localization accuracy. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computer systems organization → Special purpose systems.

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“…vi. On-board ultrasonic transducers can be used to enable acoustic localization and tracking, which are known to have better accuracy than RF-based systems because of the low propagation speed of sound in air [29]. vii.…”

Section: Introductionmentioning

confidence: 99%

U-Wear

Santagati

Melodia

2015

Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

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Wearable medical sensing devices with wireless capabilities have become the cornerstone of many revolutionary digital health applications that promise to predict and treat major diseases by acquiring and processing health information. Existing wireless wearable devices are connected through radio frequency (RF) electromagnetic wave carriers based on standards such as Bluetooth or WiFi. However, these solutions tend to almost-blindly scale down traditional wireless technologies to the body environment, with little or no attention to the peculiar characteristics of the human body and the severe privacy and security requirements of patients. We contend that this is not the only possible approach, and we present U-Wear, the first networking framework for wearable medical devices based on ultrasonic communications.U-Wear encloses a set of physical, data link and network layer functionalities that can flexibly adapt to application and system requirements to efficiently distribute information between ultrasonic wearable devices. U-Wear also offers reconfiguration functionalities at the application layer to provide a flexible platform to develop medical applications. We design two prototypes that implement U-Wear and operate in the near-ultrasonic frequency range using commercial-offthe-shelf (COTS) speakers and microphones. Despite the limited bandwidth, i.e., about 2 kHz, and COTS hardware components not optimized for operating at high frequency, our prototypes (i) achieve data rates up to 2.76 kbit/s with bit-error-rate lower than 10 −5 using a transmission power of 20 mW; (ii) enable multiple nodes to share the medium; and (iii) implement reconfigurable processing to extract medical parameters from sensors with high accuracy.

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A novel ultrasonic indoor localization system with simultaneous estimation of position and velocity

Cited by 9 publications

References 6 publications

ShoesLoc

ShoesLoc

Mmloc

U-Wear

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