Accuracy indicator for fingerprinting localization systems

Moghtadaiee, Vahideh; Dempster, Andrew G.; Li, Binghao

doi:10.1109/plans.2012.6236976

Cited by 25 publications

(25 citation statements)

References 12 publications

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“…Request permissions from Permissions@acm.org. Infrastructure-free approaches have focused on leveraging already existing WiFi [5,15,49,40,41,45,6,22,47,50], FM and TV [8,9,34,28,29,27,12,33,19,48], GSM [31,44], geo-magnetic [10], and sound signals [43] to enable indoor localization through detailed fingerprinting. Infrastructure-based approaches rely on the deployment of customized RF-beacons [37], such as RFID [30], infrared [46], ultrasound [36,20], Bluetooth [7], short-range FM transmitters [26], lights [23], and magnetic signal modulators [32,2] to enable accurate indoor position estimation.…”

Section: Introductionmentioning

confidence: 99%

A realistic evaluation and comparison of indoor location technologies

Lymberopoulos

Liu

Yang

et al. 2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

305

177

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We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study's results, discuss the current state-ofthe-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.

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Section: Introductionmentioning

confidence: 99%

A realistic evaluation and comparison of indoor location technologies

Lymberopoulos

Liu

Yang

et al. 2015

Proceedings of the 14th International Conference on Information Processing in Sensor Networks

305

177

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“…In this work, this second estimate will be referred to as the Dynamic Accuracy Estimation (DAE), since it concerns an estimation over the accuracy of produced location estimates, which is produced dynamically, as the system operates. The relevant literature refers to the Dynamic Accuracy Estimation either as an 'accuracy estimation' [16], [17], [20] an 'error estimation' [24], [13], [14], [7], [15], or even as a 'confidence' [18]. The actual, accurate measurement of the error/accuracy of a position estimate can only become available when the ground truth location is known and its distance from the location estimate produced by the positioning system is measured.…”

Section: Methodology and Terminologymentioning

confidence: 99%

“…In the first category of the rule-based methods, which monopolized the literature of the field for a long period of time, the aim has been to hand-craft analytic or heuristic methods of estimating the quality of location estimates. A variety of rule-based methods has been proposed, such as using: the average geographic distance between the nearest neighbors [13], improved by factoring in as a weight the proximity of the nearest neighbors and also introducing the location estimate [14], or by introducing a weighted average of likelihoods instead of a simple average of Euclidean distances [15]; a mechanism to calculate a Dilution-of-Precisionlike value [16]; the geographic distance from the furthest neighbor to the location estimate [17]; the spatial distribution of the latest position estimates [18], [19]; and an offline method based on the Cramer-Rao Lower Bound ratio [20].…”

Section: Related Workmentioning

confidence: 99%

Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy

Anagnostopoulos

Kalousis

2021

ICC 2021 - IEEE International Conference on Communications

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The primary expectation from positioning systems is for them to provide the users with reliable estimates of their position. An additional piece of information that can greatly help the users utilize position estimates is the level of uncertainty that a positioning system assigns to the position estimate it produced. The concept of dynamically estimating the accuracy of position estimates of fingerprinting positioning systems has been sporadically discussed over the last decade in the literature of the field, where mainly handcrafted rules based on domain knowledge have been proposed. The emergence of IoT devices and the proliferation of data from Low Power Wide Area Networks (LPWANs) have facilitated the conceptualization of data-driven methods of determining the estimated certainty over position estimates. In this work, we analyze the data-driven approach of determining the Dynamic Accuracy Estimation (DAE), considering it in the broader context of a positioning system. More specifically, with the use of a public LoRaWAN dataset, the current work analyses: the repartition of the available training set between the tasks of determining the location estimates and the DAE, the concept of selecting a subset of the most reliable estimates, and the impact that the spatial distribution of the data has to the accuracy of the DAE. The work provides a wide overview of the data-driven approach of DAE determination in the context of the overall design of a positioning system.

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“…Moreover, the Dynamic Accuracy Estimation (DAE), which expresses the estimated potential error of the provided location estimate, is often depicted as a slightly transparent circle centered at the location estimate, with a radius that corresponds to the estimated error (Figure 1). This concept is met in the relevant bibliography with the terms 'accuracy estimation' [3][4][5], 'error estimation' [6][7][8][9][10], or 'confidence' [11]. This accuracy estimation is helpful in several ways, as it facilitates the cognitive interpretation of the reliability of the provided estimates by users and it assists their decision making [11].…”

Section: Introductionmentioning

confidence: 99%

Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization

Anagnostopoulos

Kalousis

2022

Sensors

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Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The concept of estimating, dynamically, the accuracy of the position estimates provided by an IPS has been sporadically studied in the literature of the field. Recently, this concept has started being studied as well in the context of outdoor positioning systems of Internet of Things (IoT) based on Low-Power Wide-Area Networks (LPWANs). What is problematic is that the consistent comparison of the proposed methods is quasi nonexistent: new methods rarely use previous ones as baselines; often, a small number of evaluation metrics are reported while different metrics are reported among different relevant publications, the use of open data is rare, and the publication of open code is absent. In this work, we present an open-source, reproducible benchmarking framework for evaluating and consistently comparing various methods of Dynamic Accuracy Estimation (DAE). This work reviews the relevant literature, presenting in a consistent terminology commonalities and differences and discussing baselines and evaluation metrics. Moreover, it evaluates multiple methods of DAE using open data, open code, and a rich set of relevant evaluation metrics. This is the first work aiming to establish the state of the art of methods of DAE determination in IPS and in LPWAN positioning systems, through an open, transparent, holistic, reproducible, and consistent evaluation of the methods proposed in the relevant literature.

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Accuracy indicator for fingerprinting localization systems

Cited by 25 publications

References 12 publications

A realistic evaluation and comparison of indoor location technologies

A realistic evaluation and comparison of indoor location technologies

Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy

Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization

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