A High Precision Indoor Cooperative Localization Scheme Based on UWB Signals

Yong, Guofu; Cai, Zhuoran; Dong, Hao

doi:10.1007/978-3-030-19156-6_59

Cited by 3 publications

(9 citation statements)

References 6 publications

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“…Trilateration and Geometric algorithms have been mainly used in those CIPSs that attempt to improve the energy consumption [ 42 , 43 ]. The methods EKF and LS, in combination with the UWB technology, were used in [ 49 , 115 ] to improve the position precision.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…The two least represented evaluation metrics, energy consumption [ 42 , 43 , 144 ] (discussed in Section 5.2.3 ) and position precision [ 49 , 115 ] (both based on UWB technology), have only recently been considered.…”

Section: Discussionmentioning

confidence: 99%

“…The study selection process, with step-wise results, is schematically depicted using the PRISMA flow diagram in Figure 2 . As a final set of eligible studies, 84 articles [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 63 , 77 , 79 , 80 , 81 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 ,…”

Section: Methodsmentioning

confidence: 99%

“…Algorithm Bayesian F. N/S W/I E PA [ 138 ] UWB UWB TDoA TDoA Coop. Algorithm Analytic N/S W/I E PA [ 137 ] 5G 5G AoA AoA Multilateration LS D I-L S PA+CC [ 49 ] UWB UWB ToA/ToF ToA/ToF Entropy-based ToA LS D I-L S PA+PP [ 81 ] Wi-Fi Wi-Fi RSSI RSSI Ranging Multidimensional Scaling N/S …”

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

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Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

108

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Appendix A1 Search Queriesmentioning

confidence: 99%

See 3 more Smart Citations

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

108

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Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Own¹,

Hou²,

Tao³

2019

IEEE Access

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Today, indoor localization technology based on WiFi signals has become more and more popular and applicable. It not only facilitates people's lives but also creates enormous economic value. However, during the propagation of the WiFi signal, it is easily interfered by obstacles, and the signal fluctuation is significant, resulting in low accuracy of positioning. To overcome these problems, we reduce the influence of environmental factors firstly. Then the positioning accuracy is improved by using the SVM model to distinguish the NLOS or LOS environment and employing the capsule networks to derive the users' positions with the WiFi 2.4G and 5G signals. As we all know, the WiFi 2.4G signal has excellent penetrability and is less affected by obstacles, while the WiFi 5G signal has excellent stability and small fluctuations. Therefore, we use the advantages of these two kinds of signals to derive the optimal suggestion by the capsule neural network, which is the learning system with minimum data sets needed. The experimental results show that the positioning effect of the two signals simultaneously is better than the positioning effect of a single signal. We also compare with the traditional indoor positioning methods and use the simulation data to carry out the robustness test, and the positioning accuracy reached 0.99 m in the field environment finally.INDEX TERMS Indoor localization, NLOS and LOS channel propagation condition, WiFi 2.4G and WiFi 5G, SVM, capsule network.

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Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

Ding

Xiong

et al. 2022

IEEE Internet Things J.

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A High Precision Indoor Cooperative Localization Scheme Based on UWB Signals

Cited by 3 publications

References 6 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Signal Fuse Learning Method With Dual Bands WiFi Signal Measurements in Indoor Positioning

Distributed Collaborative Pedestrian Inertial SLAM With Unknown Initial Relative Poses

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