Indoor WLAN Collaborative Localization Algorithm Based on Geometric Figure Overlap

Geng, Xiaolong; Zhou, Mu; Wei, Yacong; Tang, Yunxia

doi:10.1007/978-981-10-3229-5_54

Cited by 3 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As with the non-collaborative part, the most used collaborative methods present advantages and drawbacks. The six most used methods were Particle Filter [ 83 , 89 , 93 , 98 , 102 , 117 , 118 , 120 , 123 , 126 , 129 , 136 , 146 , 147 , 149 , 149 ]; Belief Propagation [ 46 , 47 , 96 , 109 , 130 , 131 , 132 , 135 ]; EKF [ 106 , 107 , 115 , 125 , 140 , 141 , 150 ]; Geometric Algorithm [ 43 , 124 , 127 , 134 , 142 , 143 ]; LS [ 45 , 49 , 133 , 137 , 148 ]; Trilateration [ 40 , 41 , 42 , 94 ]. One of the main advantages of the methods based on Particle Filter is their capability of handling non-Gaussian and non-linear estimations; however, their computational complexity increases (increment of the number of particles) as the position accuracy increases.…”

Section: Discussionmentioning

confidence: 99%

“…Algorithm Geom. Algorithm C W/I S+E PA 2018 [ 134 ] Wi-Fi Wi-Fi F. printing RSSI KNN Geom. Algorithm N/S I-L E PA [ 48 ] Wi-Fi Other RF RSSI TDoA RSSI-B.…”

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

“…Regarding the metrics, all the reviewed works included the positioning accuracy, as we can observe in Figure 4d. Going deeper in this particular evaluation metric, we observe that different measures have been provided which, in descending order, are as follows: the Cumulative distribution Function (CDF) of the positioning error [43,44,79,80,108,112,113,116,118,119,126,129,131,134,150], the Root Mean Square Error (RMSE) [48,49,77,109,121,122,126,135], the standard deviation of the error [40][41][42]63,80,110], the minimum mean square error [46,117,129,130,133], and finally the average positioning error [105,123,132,134]. This makes comparison of accuracy unfeasible.…”

Section: Evaluation Of Collaborative Indoor Position Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

View full text Add to dashboard Cite

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Algorithm Geom. Algorithm C W/I S+E PA 2018 [ 134 ] Wi-Fi Wi-Fi F. printing RSSI KNN Geom. Algorithm N/S I-L E PA [ 48 ] Wi-Fi Other RF RSSI TDoA RSSI-B.…”

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

Section: Evaluation Of Collaborative Indoor Position Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

View full text Add to dashboard Cite

show abstract

“…An improvement of the results of localization can include a variety of the techniques. The examples include error compensation for the body-shadowing effect [45], continuous feature scaling and outlier deletion [46], collaborative localization using information from multiple nodes [47], error correction using collision avoidance velocity and map-aided inertial dead reckoning (DR) [48], probabilistic fingerprint (P-FP) using the probability density functions of the received signal strength algorithm (RSSA) [49], the use of optimization algorithms to decrease the localization error considering different RSS thresholds for hybrid indoor positioning [50], and particle swarm optimization (PSO) for fitting the signal attenuation curve, thus allowing the developed parametric model to locate the user’s position with the standard deviation of positioning of 1.15 m [51].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings

Almadani

Orujov

Maskeliūnas

et al. 2019

Sensors

View full text Add to dashboard Cite

The ability to precisely locate and navigate a partially impaired or a blind person within a building is increasingly important for a wide variety of public safety and localization services. In this paper, we explore indoor localization algorithms using Bluetooth Low Energy (BLE) beacons. We propose using the BLE beacon’s received signal strength indication (RSSI) and the geometric distance from the current beacon to the fingerprint point in the framework of fuzzy logic for calculating the Euclidean distance for the subsequent determination of location. According to our results, the fingerprinting algorithm with fuzzy logic type-2 (hesitant fuzzy sets) is fit for use as an indoor localization method with BLE beacons. The average error of localization is only 0.43 m, and the algorithm obtains a navigation precision of 98.2 ± 1%. This precision confirms that the algorithms provide great aid to a visually impaired person in unknown spaces, especially those designed without physical tactile guides, as confirmed by low Fréchet and Hausdorff distance values and high navigation efficiency index (NEI) scores.

show abstract

Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

Ding

Xiong

et al. 2022

IEEE Internet Things J.

View full text Add to dashboard Cite

Indoor WLAN Collaborative Localization Algorithm Based on Geometric Figure Overlap

Cited by 3 publications

References 2 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings

Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

Contact Info

Product

Resources

About