Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model

Zampella, Francisco; Jiménez, Antonio R.; Granja, Fernando Seco

doi:10.1109/tvt.2015.2391296

Cited by 84 publications

(57 citation statements)

References 25 publications

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“…Although RSS-based tracking with Kalman filters (KF) is a well-established area, it is still an interesting research topic due to its various new applications for accurate localization [21], [22]. In [23] two-slope RSS model is used with two Extended Kalman Filters (EKF) considering an IMM framework to improve tracking accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Çiftler

Dikmese

Güvenç

et al. 2018

IEEE Internet Things J.

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Abstract-Zone-level occupancy counting is a critical technology for smart buildings and can be used for several applications such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building as well as an established network. In this study, in order to achieve occupancy counting, we consider the use of WiFi probe requests that are continuously transmitted from WiFi enabled smart devices. To this end, WiFi Pineapple equipment are used for passively capturing ambient probe requests from WiFi devices such as smart phones and tablets, where no connectivity to a WiFi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multi-model Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using WiFi data collected at a university building show that utilization of WiFi probe requests can be considered a viable solution tool for zone-level occupancy counting in smart buildings.

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Section: Literature Reviewmentioning

confidence: 99%

Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Çiftler

Dikmese

Güvenç

et al. 2018

IEEE Internet Things J.

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“…The state information is required to be able to adjust the navigation filter parameters like the propagation [17] or the update model. The state consists of flags, shown in Table I, and of the position solution that is the northing and the easting on a local tangent plane.…”

Section: B Context Engine Objectmentioning

confidence: 99%

A tripartite filter design for seamless pedestrian navigation using recursive 2-means clustering and Tukey update

Peltola

Xiao

Hill

et al. 2018

2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

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Abstract-Mobile devices are desired to guide users seamlessly to diverse destinations indoors and outdoors. The positioning fixing subsystems often provide poor quality measurements with gaps in an urban environment. No single position fixing technology works continuously. Many sensor fusion variations have been previously trialed to overcome this challenge, including the particle filter that is robust and the Kalman filter which is fast. However, a lack exists, of context aware, seamless systems that are able to use the most fit sensors and methods in the correct context. A novel adaptive and modular tripartite navigation filter design is presented to enable seamless navigation. It consists of a sensor subsystem, a context inference and a navigation filter blocks. A foot-mounted inertial measurement unit (IMU), a Global Navigation Satellite System (GNSS) receiver, Bluetooth Low Energy (BLE) and Ultrawideband (UWB) positioning systems were used in the evaluation implementation of this design. A novel recursive 2-means clustering method was developed to track multiple hypotheses when there are gaps in position fixes. The closest hypothesis to a new position fix is selected when the gap ends. Moreover, when the position fix quality measure is not reliable, a fusion approach using a Tukey-style particle filter measurement update is introduced. Results show the successful operation of the design implementation. The Tukey update improves accuracy by 5% and together with the clustering method the system robustness is enhanced.

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“…Since there is no literature discussion on the pros and cons of using iBeacons (or other local radio emitter) coupled with smartphones or instrumented vehicles, instead, there is extensive literature [47,48] on how to use iBeacons to calculate approximate indoor distances and estimate indoor location; we present briefly the results of a first preliminary experimentation with iBeacons and vehicles.…”

Section: The Use Of Local Radio Signals For Car Positioningmentioning

confidence: 99%

The Use of Adaptive Traffic Signal Systems Based on Floating Car Data

Astarita

Giofrè

Guido

et al. 2017

Wireless Communications and Mobile Computing

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This paper presents a simple concept which has not been, up to now, thoroughly explored in scientific research: the use of information coming from the network of Internet connected mobile devices (on vehicles) to regulate traffic light systems. Three large-scale changes are going to shape the future of transportation and could lead to the regulation of traffic signal system based on floating car data (FCD): (i) the implementation of Internet connected cars with global navigation satellite (GNSS) system receivers and the autonomous car revolution; (ii) the spreading of mobile cooperative Web 2.0 and the extension to connected vehicles; (iii) an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety, and environmental issues. Up to now, the concept of floating car data (FCD) has only been extensively used to obtain traffic information and estimate traffic parameters. Traffic lights regulation based on FCD technology has not been fully researched since the implementation requires new ideas and algorithms. This paper intends to provide a seminal insight into the important issue of adaptive traffic light based on FCD by presenting ideas that can be useful to researchers and engineers in the long-term task of developing new algorithms and systems that may revolutionize the way traffic lights are regulated.

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Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model

Cited by 84 publications

References 25 publications

Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

Occupancy Counting With Burst and Intermittent Signals in Smart Buildings

A tripartite filter design for seamless pedestrian navigation using recursive 2-means clustering and Tukey update

The Use of Adaptive Traffic Signal Systems Based on Floating Car Data

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