Positioning Error Prediction and Training Data Evaluation in RF Fingerprinting Method

Khandker, Syed; Mondal, Riaz Uddin; Ristaniemi, Tapani

doi:10.1109/ipin.2019.8911821

Cited by 6 publications

(13 citation statements)

References 21 publications

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“…In the on-line phase, the position was estimated using multiple Nearest Neighbour (k-NN). Twenty years later, Wi-Fi FP and methods based on k-NN are still very popular [19,34,35,36].…”

Section: Background and Related Workmentioning

confidence: 99%

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

Torres-Sospedra

Richter

Moreira

et al. 2022

IEEE Trans. on Mobile Comput.

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Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only k-means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

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“…In the on-line phase, the position was estimated using multiple Nearest Neighbour (k-NN). Twenty years later, Wi-Fi FP and methods based on k-NN are still very popular [19,34,35,36].…”

Section: Background and Related Workmentioning

confidence: 99%

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

Torres-Sospedra

Richter

Moreira

et al. 2022

IEEE Trans. on Mobile Comput.

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“…The categorical division of these approaches can be described by two main types of methods: the rule-based methods (Section 2.1) and the data-driven methods (Section 2.2). In the first category of the rule-based methods [4,5,7,8,10,11,[20][21][22], which monopolized the publications of the field for a long period of time, the aim is to hand-craft heuristic methods of estimating the quality of location estimates. The second category of the data-driven methods [6,9,16,17] has only recently emerged.…”

Section: Related Workmentioning

confidence: 99%

“…Both IPS and outdoor LPWAN-based systems commonly utilize signals from basestations to infer location estimates [18,19]. Over the last decade, there have been several publications that propose different methods of calculating the DAE in such systems, which are presented in this work [4][5][6][7][8][9][10][11]16,17,[20][21][22]. Nevertheless, as the current study demonstrates, the comparison of each new method with the previously published ones is quasi nonexistent.…”

Section: Introductionmentioning

confidence: 97%

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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“…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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Positioning Error Prediction and Training Data Evaluation in RF Fingerprinting Method

Cited by 6 publications

References 21 publications

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

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

Analysing the Data-Driven Approach of Dynamically Estimating Positioning Accuracy

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