Joint Time-Frequency RSSI Features for Convolutional Neural Network-Based Indoor Fingerprinting Localization

Soro, Bedionita; Lee, Chae-Woo

doi:10.1109/access.2019.2932469

Cited by 29 publications

(16 citation statements)

References 30 publications

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“…These transformations have been used in several works, e.g., [208], [212], [214]. Wavelet transform is another method that has been used to denoise the received signal and to provide higher dimensional representation, e.g., from 1D time series vector to 2D time-frequency "images", as in [271], [307] where they use the wavelet to transform the RSSI readings in WiFi system.…”

Section: E Feature Representationmentioning

confidence: 99%

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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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Section: E Feature Representationmentioning

confidence: 99%

“…Viewing the RSSI values as time series, Ref. [307] uses CWT (see Sec. IV) to produce a 2D time-frequency image; the CNN predicts the closest reference points to the target, and KNN is then used to infer the coordinates of the target.…”

Section: Supervisedmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Burghal,

Ravi,

Rao

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Another direction to enhance RSSI localization is to use pattern matching and fingerprinting based methods for reducing the influence of range measurement errors [ 17 , 19 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 37 , 38 , 39 , 40 , 41 ]. The LANDMARC indoor localization system is presented in [ 22 ] as a pattern matching method to enhance the overall accuracy of locating objects using some reference tags.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [ 40 ] proposes a Gaussian filtering algorithm based on an extreme learning machine to address the problem of inaccurate indoor positioning when significant RSSI fluctuations happen during the measurement process. In [ 26 ], continuous wavelet transform is used to extract time-frequency or time-scaling information from each RSSI samples for indoor fingerprinting localization algorithm. The work in [ 27 ] proposes to use the feature-scaling-based k-nearest neighbor algorithm with RSSI.…”

Section: Related Workmentioning

confidence: 99%

“…Generous research effort has been devoted to developing localization methods that can compensate for the adverse effects of uncertainties in RSSI measurements [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Most of these proposed methods depend on the optimization techniques or fingerprinting, which requires high computational complexity, making them unpractical for IoT localization applications.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

El-Absi

Zheng

Abuelhaija

et al. 2020

Sensors

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Indoor localization based on unsynchronized, low-complexity, passive radio frequency identification (RFID) using the received signal strength indicator (RSSI) has a wide potential for a variety of internet of things (IoTs) applications due to their energy-harvesting capabilities and low complexity. However, conventional RSSI-based algorithms present inaccurate ranging, especially in indoor environments, mainly because of the multipath randomness effect. In this work, we propose RSSI-based localization with low-complexity, passive RFID infrastructure utilizing the potential benefits of large-scale MIMO technology operated in the millimeter-wave band, which offers channel hardening, in order to alleviate the effect of small-scale fading. Particularly, by investigating an indoor environment equipped with extremely simple dielectric resonator (DR) tags, we propose an efficient localization algorithm that enables a smart object equipped with large-scale MIMO exploiting the RSSI measurements obtained from the reference DR tags in order to improve the localization accuracy. In this context, we also derive Cramer–Rao lower bound of the proposed technique. Numerical results evidence the effectiveness of the proposed algorithms considering various arbitrary network topologies, and results are compared with an existing algorithm, where the proposed algorithms not only produce higher localization accuracy but also achieve a greater robustness against inaccuracies in channel modeling.

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A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

Zhang

Zhao

2021

Analog Integr Circ Sig Process

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Joint Time-Frequency RSSI Features for Convolutional Neural Network-Based Indoor Fingerprinting Localization

Cited by 29 publications

References 30 publications

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Indoor Large-Scale MIMO-Based RSSI Localization with Low-Complexity RFID Infrastructure

A 392-pW 42.7-dB Gm-C wavelet filter for low-frequency feature extraction used for wearable sensor

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