3D WLAN indoor positioning in multi-storey buildings

Gansemer, Sebastian; Hakobyan, Syuzanna; Puschel, S.; Grosmann, U.

doi:10.1109/idaacs.2009.5342893

Cited by 18 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 27 ], the floor was chosen that had the database RSS vector with the highest similarity to the measured RSS vector. However, detection accuracy depends on the environment and the available APs.…”

Section: Related Workmentioning

confidence: 99%

“…Another challenge in an indoor pedestrian localisation system is detecting floor transitions and determining the correct floor number (i.e., floor detection), thus allowing a pedestrian to be tracked across multiple floors. This can be achieved by detecting the floor number directly using (WiFi) fingerprinting [ 25 , 27 ] or by detecting floor transition events using data from onboard sensors [ 24 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Cock

Joseph

Martens

et al. 2021

Sensors

View full text Add to dashboard Cite

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Cock

Joseph

Martens

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Gansemer, Hakobyan, Puschel & Grosmann, (2009) [11] , in their experiment used ISO Line and Euclidean algorithm for floor determination and they claimed that using Euclidean distance algorithm they determined exact floor with 100% accuracy. Schulcz, Varga & Tóth (2010) [10] , developed IPS by combining empirical propagation model with fingerprinting, they also utilized accelerometer and magnetometer and achieved an accuracy about 3.09m.…”

Section: Literature Reviewmentioning

confidence: 99%

3-Dimensional Indoor Positioning System based on WI-FI Received Signal Strength using Greedy Algorithm and Parallel Resilient Propagation

Alam¹,

Atif²,

Hussain³

et al. 2015

IJCA

View full text Add to dashboard Cite

The importance of services, based on current location of objects is growing. This is because Global Navigation Satellite System (GNSS) cannot provide object position inside buildings. In modern era the Location Based Services (LBS) are tremendously dependent on Indoor Positioning System (IPS). Parallel RPROP and greedy algorithm were combined for development of IPS using Received Signal Strength (RSS) in heterogeneous environment, the environment comprised of human activity, walls material, cupboards, and various type of surveying machines etc. The propagation of Wi-Fi signal varies directionally, therefore to cope with direction changes in signals; this proposed model produces three sets of weights, which could be considered best for easting, northing and height respectively. Proposed model was trained with 75% of collected data and tested on remaining 25% data. Distance error between known points and predicted coordinates was used for accuracy assessment. Through experiments a maximum accuracy of 0.87m was achieved and it was found that median error was less than mean error. Median error between known points and predicted coordinates was about 3.32m and their mean error was about 4.62m, which is satisfactory as far as 3D position determination is concerned. On the basis of results the use of parallel RPROP and greedy algorithm for 3D position determination in heterogeneous environment is recommended.

show abstract

“…Gansemer et al [18] pointed out that the 3D indoor positioning is more realized by UWB, RFID, and other technologies. They stressed the need for 3D WLAN indoor positioning and proposed a method that extends isolines algorithm [19], used in 2D WLAN indoor positioning to 3D space.…”

Section: Related Workmentioning

confidence: 99%

A New Three-Dimensional Indoor Positioning Mechanism Based on Wireless LAN

Cheng

Cai

Zhang

et al. 2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The researches on two-dimensional indoor positioning based on wireless LAN and the location fingerprint methods have become mature, but in the actual indoor positioning situation, users are also concerned about the height where they stand. Due to the expansion of the range of three-dimensional indoor positioning, more features must be needed to describe the location fingerprint. Directly using a machine learning algorithm will result in the reduced ability of classification. To solve this problem, in this paper, a “divide and conquer” strategy is adopted; that is, first through k-medoids algorithm the three-dimensional location space is clustered into a number of service areas, and then a multicategory SVM with less features is created for each service area for further positioning. Our experiment shows that the error distance resolution of the approach with k-medoids algorithm and multicategory SVM is higher than that of the approach only with SVM, and the former can effectively decrease the “crazy prediction.”

show abstract

3D WLAN indoor positioning in multi-storey buildings

Cited by 18 publications

References 6 publications

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

3-Dimensional Indoor Positioning System based on WI-FI Received Signal Strength using Greedy Algorithm and Parallel Resilient Propagation

A New Three-Dimensional Indoor Positioning Mechanism Based on Wireless LAN

Contact Info

Product

Resources

About