Application of particle filters for indoor positioning using floor plans

Davidson, Pavel; Collin, Jussi; Takala, Jarmo

doi:10.1109/upinlbs.2010.5653830

Cited by 54 publications

(32 citation statements)

References 6 publications

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“…Due to the complexity of many indoor environments multipath arises, which decreases the positioning accuracy. Current state-of-the-art tries to cope with this by making use of advanced processing techniques, e.g., Kalman filters [6], [12], particle filters [13]- [15] and machine learning techniques [16], but all aforementioned approaches neglect the influence of the human body itself. In [17], a body shadowing mitigation method is used on top of an RSSI-based Monte Carlo localization technique, achieving meter scale accuracies for a wrist-worn personnel tracking tag.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Indoor Location Tracking Through Body Shadowing Compensation

et al. 2016

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Abstract-This paper presents a radio-frequency (RF) based location tracking system that improves its performance by eliminating the shadowing caused by the human body of the user being tracked. The presence of such a user will influence the RF signal paths between a body-worn node and the receiving nodes. This influence will vary with the user's location and orientation and, as a result, will deteriorate the performance regarding location tracking. By using multiple mobile nodes, placed on different parts of a human body, we exploit the fact that the combination of multiple measured signal strengths will show less variation caused by the user's body. Another method is to compensate explicitly for the influence of the body by using the user's orientation towards the fixed infrastructure nodes. Both approaches can be independently combined and reduce the influence caused by body shadowing, hereby improving the tracking accuracy. The overall system performance is extensively verified on a building-wide testbed for sensor experiments. The results show a significant improvement in tracking accuracy. The total improvement in mean accuracy is 38.1% when using three mobile nodes instead of one and simultaneously compensating for the user's orientation.

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Section: Related Workmentioning

confidence: 99%

Enhanced Indoor Location Tracking Through Body Shadowing Compensation

et al. 2016

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“…For example, the studies by Gusenbauer et al (2010), Bird andArden (2011), Goyal et al (2011) and Diaz et al (2014), among others, used Kalman filter. Another algorithm is the Particle Filter (PF) (Harle, 2013) used in the studies of Davidson, Collin, and Takala (2010) and Leppäkoski et al (2013). The Kalman filter performs the statistical combination of INS information with other methods in hybrid to track drifting parameters of the sensors in the INS (Grewal et al, 2007), while particle filter provides a way for map information to be fused with pedestrian position information (Harle, 2013;Leppäkoski et al, 2013;Davidson et al, 2010).…”

Section: Pedestrian Dead Reckoning (Pdr)mentioning

confidence: 99%

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

Sakpere

Oshin

Mlitwa

2017

SACJ

148

129

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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.

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“…6 the map aided positioning using wheel speed information and road map information is demonstrated, where GPS information is used as a ground truth reference only. For other map aided positioning applications see for instance [42][43][44][45][46][47][48]. First the PF is initialized in the vicinity of the GPS position.…”

Section: B Particle Filter Based Map Aided Positioningmentioning

confidence: 99%