Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Khalajmehrabadi, Ali; Gatsis, Nikolaos; Akopian, David

doi:10.1109/comst.2017.2671454

Cited by 292 publications

(191 citation statements)

References 169 publications

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“…Therefore, it makes the accuracy of fingerprinting-based localization higher than other techniques. Fingerprinting based positioning systems are reviewed comprehensively in [36], [75], [109]. 5) Direction of Arrival (DoA) Technique: DoA ranging measurements are based on the angle of the received signal at the receiver [110].…”

Section: Fundamental Ranging Schemesmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

137

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Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks. 2 MDS is much popular among all these techniques because of its simplicity and many application areas. MDS analysis finds the spatial map for objects given that the similarity or dissimilarity information between the objects is available [16].In the recent past, MDS is widely used for localization and mapping of wireless sensor networks (WSNs) and the internet of things (IoT). In [17] a proximity information based sensor network localization is proposed, where the main idea is to construct a local configuration of sensor nodes by using classical MDS (CMDS). The MDS based localization algorithms in [17] and [18] are centralized with higher computational complexity [7]. Semi-centralized (or clustered) MDS techniques are developed to compute local coordinates of nodes, which then are refined to find the final position of the nodes [19], [20]. In [21], [22] and [23] the authors proposed manifold learning to estimate the sensor nodes position in wireless sensor networks. In [24] the authors proposed Nystrom approximation for the proximity information matrix in MDS to reduce its size for better localization accuracy in sensor networks. Distributed MDS based localization algorithm is proposed in [25] with noisy range measurements, where the authors assume that the distances are corrupted with independent Gaussian random noise. MDS methods with different refinement schemes have also been proposed in the literature t...

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Section: Fundamental Ranging Schemesmentioning

confidence: 99%

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Saeed

Nam

Al-Naffouri

et al. 2019

IEEE Commun. Surv. Tutorials

137

View full text Add to dashboard Cite

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“…channel impulse response, propagation delay, angle of arrival or received signal strength-at one or multiple positions in the network, typically BSs or access points (APs). This fingerprint of the user location can be then either compared to existing databases and maps [2], [3], or subsequently intepreted in a geometric sense. The assumption of the first approach is that there should be a bijective relationship between the user position and the fingerprint, although this relationship is usually too rich to be modeled and must thus be learned.…”

Section: Introductionmentioning

confidence: 99%

CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach

Decurninge

Ordóñez

Ferrand

et al. 2018

2018 15th International Symposium on Wireless Communication Systems (ISWCS)

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We report on experimental results on the use of a learning-based approach to infer the location of a mobile user of a cellular network within a cell, for a 5G-type Massive multiple input, multiple output (MIMO) system. We describe how the sample spatial covariance matrix computed from the CSI can be used as the input to a learning algorithm which attempts to relate it to user location. We discuss several learning approaches, and analyze in depth the application of extreme learning machines, for which theoretical approximate performance benchmarks are available, to the localization problem. We validate the proposed approach using experimental data collected on a Huawei 5G testbed, provide some performance and robustness benchmarks, and discuss practical issues related to the deployment of such a technique in 5G networks.

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“…Thus, different approaches have been proposed (comp. [1], [2], [3], [4], [5], [6]) and investigated in the past, each optimized for different applications and system models (e.g., see [2] and [5] and references therein). Moreover, geomagnetic sensors were tested in [3] as an indoor positioning system in combination with deep learning.…”

Section: Introductionmentioning

confidence: 99%

On Deep Learning-Based Massive MIMO Indoor User Localization

Arnold

Dörner

Brink

2018

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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We examine the usability of deep neural networks for multiple-input multiple-output (MIMO) user positioning solely based on the orthogonal frequency division multiplex (OFDM) complex channel coefficients. In contrast to other indoor positioning systems (IPSs), the proposed method does not require any additional piloting overhead or any other changes in the communications system itself as it is deployed on top of an existing OFDM MIMO system. Supported by actual measurements, we are mainly interested in the more challenging non-line of sight (NLoS) scenario. However, gradient descent optimization is known to require a large amount of data-points for training, i.e., the required database would be too large when compared to conventional methods. Thus, we propose a twostep training procedure, with training on simulated line of sight (LoS) data in the first step, and finetuning on measured NLoS positions in the second step. This turns out to reduce the required measured training positions and thus, reduces the effort for data acquisition.

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Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Cited by 292 publications

References 169 publications

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach

On Deep Learning-Based Massive MIMO Indoor User Localization

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