TDoA‐Based Outdoor Positioning with Tracking Algorithm in a Public LoRa Network

Podevijn, Nico; David, Paul A.; Trogh, Jens; Martens, Luc; Suanet, Pieter; Hendrikse, Kim; Joseph, Wout

doi:10.1155/2018/1864209

Cited by 92 publications

(71 citation statements)

References 19 publications

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“…Plets et al [9], have evaluated experimentally RRS and TDoA ranging positioning methods using a LoRaWAN network, reporting median errors of 1250 and 200 meters for RRS and TDoA respectively. Similarly, Podevinj et al [10] present a tracking algorithm which uses a TDoA ranging method for its initial raw estimates that feed the tracking module, reporting a similar median performance (200m) on the raw localization estimates. The two aforementioned works however do not report the mean error of their systems.…”

Section: Related Workmentioning

confidence: 93%

See 1 more Smart Citation

A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning

Anagnostopoulos

Kalousis

2019

2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPWAN) such as Sigfox, have been used for localization. In this rather recent field of study, not many publicly available datasets, which would facilitate the consistent comparison of different positioning systems, exist so far. In the current study, a published dataset of RSSI measurements on a Sigfox network deployed in Antwerp, Belgium is used to analyse the appropriate selection of preprocessing steps and to tune the hyperparameters of a kNN fingerprinting method. Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented. In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of out of range values. With the appropriate tuning of these factors, the achieved mean localization error was 298 meters, and the median error was 109 meters. To facilitate the reproducibility of tests and comparability of results, the code and train/validation/test split used in this study are available.

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Section: Related Workmentioning

confidence: 93%

“…It is reminded that the default value of α is 24. Integer values in the range [10,40] have been evaluated in the best configuration found so far, concerning the exponential data representation. Thus, the Bray-Curtis distance has been used with k = 5 and τ = −157.…”

Section: Parameters a And B Of The Exponential And Eowed Data Reprmentioning

confidence: 99%

A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning

Anagnostopoulos

Kalousis

2019

2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…Thus, a high level of performance is achieved within a communication system in which the frequency band is limited. There are a number of papers in the literature [20][21][22][23][24][25][26][27] dealing with LoRa, but none investigates the issue of scalability.…”

Section: Large-scale Lora Sensors For Long-range Transmissionsmentioning

confidence: 99%

LoRa (Long-Range) High-Density Sensors for Internet of Things

Lavric

2019

Journal of Sensors

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Over the past few years, there has been a growing awareness regarding the concept of Internet of Things (IoT), which involves connecting to the Internet various objects surrounding us in everyday life. The main purpose of this concept closely connected to the smart city issue is increasing the quality of life by contributing to streamlining resource consumption and protecting the environment. The LoRa communication mechanism is a physical layer of the LoRaWAN protocol, defined by the LoRa Alliance. Compared to other existing technologies, LoRa is a modulation technique enabling the transfer of information over a range of tens of kilometers. The main contribution this paper brings to the field is analyzing the scalability of the LoRa technology and determining the maximum number of sensors which can be integrated into this type of monitoring and control architecture. The sensor architecture is specific to the smart city concept that involves the integration of a large number of high-density sensors distributed on a large-scale geographic area. The reason behind this study is the need to assess the scalability of the LoRa technology, taking into consideration other factors, such as the packet payload size, the duty circle parameter, the spreading factor, and the number of nodes. The experimental results reveal that the maximum number of LoRa sensors that can communicate on the same channel is 1,500; furthermore, in order to obtain a high performance level, it is necessary to schedule and plan the network as carefully as possible. The spreading factor must be allocated according to the distance at which the sensor is placed from the gateway.

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“…In range-based techniques, time difference of arrival (TDoA), time of arrival (ToA), angle of arrival (AoA), and received signal strength indicator (RSSI) are dominant [1][2][3][4][5][6][7]. TDoA and ToA are sensitive to timing errors; thus, accurate time synchronization between the readers (receivers) of these IoT devices (senders) is crucial.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Alma'aitah

Alsaify

Bani-Hani

2019

Sensors

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Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of 10 m × 10 m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

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TDoA‐Based Outdoor Positioning with Tracking Algorithm in a Public LoRa Network

Cited by 92 publications

References 19 publications

A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning

A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning

LoRa (Long-Range) High-Density Sensors for Internet of Things

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

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