A Neural Network Approach for Indoor Fingerprinting-Based Localization

Jaafar, Rayana H.; Saab, Samer S.

doi:10.1109/uemcon.2018.8796646

Cited by 19 publications

(12 citation statements)

References 18 publications

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“…A variety of deep models have been investigated. The unprocessed RSS vector can be used as input for feedforward neural networks [13], [14] or be preprocessed to obtain a low-dimensional embedding via stacked autoencoders (SAEs) [12], [15]. Convolutional neural networks can be applied using 1-D convolution on time-series RSS [16] or directly over the RSS vector [12].…”

Section: A Deep Learning For Fingerprintingmentioning

confidence: 99%

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DeepLocBIM: Learning Indoor Area Localization Guided by Digital Building Models

Laska

Blankenbach

2022

IEEE Internet Things J.

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Fingerprinting-based indoor localization is a costeffective approach to provide coarse-grained indoor location information for pedestrian mass-market applications without the requirement of installing additional positioning infrastructure. While most solutions aim at pinpointing the exact location of a user, estimating a zone/area is a promising approach to achieve a more reliable prediction. Area localization predominantly utilizes a predetermined building model segmentation to obtain zone/area labels for collected fingerprints. We propose a novel approach to multifloor indoor area localization by directly predicting polygon zones that contain the position of the user. Our model learns to construct the zones from the wall segments and thus predicted areas have a high conformity to the underlying building model (semantic expressiveness). On a self-collected as well as on a public fingerprinting data set, we compare our model with two reference approaches. We demonstrate that the utilized surface areas of the polygons are on average up to 50% smaller than those of the reference models and provide a high semantic expressiveness without requiring manual floor plan segmentation.

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Section: A Deep Learning For Fingerprintingmentioning

confidence: 99%

“…We utilize those to manually extract the most characteristic walls for the training of DLBIM. In total, we have 97 horizontal walls where (27,21,17,18,14) represents the tuple of walls per floor in ascending floor order. The total amount of vertical walls is 194 with a distribution over floors as (41,46,44,43,20).…”

Section: A Data Setsmentioning

confidence: 99%

DeepLocBIM: Learning Indoor Area Localization Guided by Digital Building Models

Laska

Blankenbach

2022

IEEE Internet Things J.

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“…Point estimation : Xiao et al [ 31 ] modeled the problem as regression task and applied a deep MLP model to estimate the position. Jaafar and Saab [ 32 ] realized point estimation using a MLP regression model after initial room classification. Using the data collected during walking along a predefined path, Sahar and Han [ 33 ] as well as Xu et al [ 34 ], Elbes et al [ 35 ], and Chen et al [ 15 ] utilized LSTM with a regression output layer to predict the exact position.…”

Section: Related Workmentioning

confidence: 99%

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Laska

Blankenbach

2021

Sensors

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Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

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“…ML has been used in fingerprinting solutions since its infancy, where standard ML methods have been utilized as matching mechanism, e.g., [6], [315]. Since then it has been utilized in other aspects as well, for instance for feature extraction and radio-map construction [72], [72], [91], [94], [103], [138], [142], [154], [231], [297], radio-map updating [85], [122], [132], [181], hierarchical solutions [75], [151], [222], [256], [316], and robust matching [79], [86], [88]. This is expected because fingerprinting systems, similar to ML, are data-driven and both may 9 operate in a training (offline) phase, and an online phase.…”

Section: F Features Utilization In Conventional and Ml-based Localiza...mentioning

confidence: 99%

“…Ref. [151] uses a hierarchical localization solution, where for each indoor location, an NN is trained. During the online phase, the real-time RSSI measurements (from WiFi or/and cellular) are first used to identify the environment, and then they are passed as input to the corresponding trained NN.…”

Section: Supervisedmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Burghal,

Ravi,

Rao

et al. 2020

Preprint

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The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets.A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field.This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.

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A Neural Network Approach for Indoor Fingerprinting-Based Localization

Cited by 19 publications

References 18 publications

DeepLocBIM: Learning Indoor Area Localization Guided by Digital Building Models

DeepLocBIM: Learning Indoor Area Localization Guided by Digital Building Models

DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

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