TDOA-TWR based positioning algorithm for UWB localization system

Kołakowski, Marcin; Djaja-Jośko, Vitomir

doi:10.1109/mikon.2016.7491981

Cited by 25 publications

(18 citation statements)

References 9 publications

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“…A majority of research focuses on positioning scenarios in existing buildings, some of them re-using already existing infrastructure (i.e., signals of opportunity). Typical scenarios that are receiving a growing interest involve locating people within houses, offices [ 63 , 115 , 123 , 131 ], and universities [ 40 , 43 , 63 , 79 , 98 , 101 , 119 , 120 , 128 , 131 ], where the viability of installing complex infrastructure is low in terms of costs, unlike industrial or warehouse scenarios, which are capable of developing and deploying robust and costly infrastructures designed for positioning. As a result, our review indeed shows that infrastructure-less approaches are predominantly selected for CIPSs in research (see Figure 4 c), and that the majority of technologies used (see Figure 5 and Figure 6 ) are already present in the environment.…”

Section: Discussionmentioning

confidence: 99%

“…The remaining 18 articles were fully collaborative, as they relayed the position estimation of the nodes and/or actors completely to a collaborative method (i.e., denoted as “Cooperative methods” in Figure 5 ). In other words, stand-alone positioning was not performed in these systems, and the collected raw data were directly processed using the corresponding proposed collaborative method [ 85 , 88 , 90 , 95 , 96 , 99 , 105 , 112 , 115 , 121 , 122 , 126 , 133 , 138 , 139 , 140 , 142 , 148 ]. Therefore, the cooperative methods are excluded from the discussion in this section devoted to the non-collaborative part; for full details on the collaborative systems, please see Section 5.2.2 .…”

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%

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Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

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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: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

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“…In [ 11 ], an UWB positioning system that supports up to 30 UWB tags in a TDMA access scheme has been developed, but their system is not designed for allowing larger user densities and they only investigate the use of TDoA approaches. A study of the benefits of a hybrid TDoA-TWR approach is illustrated in [ 12 ]. In particular, a hybrid algorithm was simulated and afterwards the results were compared with a standard TDoA approach.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities

Ridolfi

Velde

Steendam

et al. 2018

Sensors

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Radio frequency (RF) technologies are often used to track assets in indoor environments. Among others, ultra-wideband (UWB) has constantly gained interest thanks to its capability to obtain typical errors of 30 cm or lower, making it more accurate than other wireless technologies such as WiFi, which normally can predict the location with several meters accuracy. However, mainly due to technical requirements that are part of the standard, conventional medium access strategies such as clear channel assessment, are not straightforward to implement. Since most scientific papers focus on UWB accuracy improvements of a single user, it is not clear to which extend this limitation and other design choices impact the scalability of UWB indoor positioning systems. We investigated the scalability of indoor localization solutions, to prove that UWB can be used when hundreds of tags are active in the same system. This paper provides mathematical models that calculate the theoretical supported user density for multiple localization approaches, namely Time Difference of Arrival (TDoA) and Two-Way Ranging (TWR) with different MAC protocol combinations, i.e., ALOHA and TDMA. Moreover, this paper applies these formulas to a number of realistic UWB configurations to study the impact of different UWB schemes and settings. When applied to the 802.15.4a compliant Decawave DW1000 chip, the scalability dramatically degrades if the system operates with uncoordinated protocols and two-way communication schemes. In the best case scenario, UWB DW1000 chips can actively support up to 6171 tags in a single domain cell (no handover) with well-selected settings and choices, i.e., when adopting the combination of TDoA (one-way link) and TDMA. As a consequence, UWB can be used to simultaneously localize thousands of nodes in a dense network. However, we also show that the number of supported devices varies greatly depending on the MAC and PHY configuration choices.

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“…At present, time difference of arrival (TDoA) [51] is often used to realize ultra-wideband positioning. Assuming that there are four fixed base stations in the threedimensional space, the position of the tag is (x, y, z), the position of the ith base station is (x i , y i , z i ), and the distance between the tag and the base station is Ri (i = 1, 2,…, N, N = 4).…”

Section: Localization Algorithmmentioning

confidence: 99%

Ensemble learning particle swarm optimization for real-time UWB indoor localization

Cai

Zhang

2018

J Wireless Com Network

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This paper presents an ensemble learning particle swarm optimization (ELPSO) algorithm for real-time indoor localization based on ultra-wideband (UWB). Indoor localization problem can be formulated as an optimization problem to predict the target. The proposed algorithm expands the original PSO into ELPSO under superbest guide, which is a parameter employed to identify the top gbest by learning from three individual algorithms and updated asynchronously. The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms. The feasibility of the proposed ELPSO is demonstrated in both 2D and 3D UWB indoor localization system generating promising results.

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TDOA-TWR based positioning algorithm for UWB localization system

Cited by 25 publications

References 9 publications

Collaborative Indoor Positioning Systems: A Systematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities

Ensemble learning particle swarm optimization for real-time UWB indoor localization

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