D<sub>FOPS</sub>: Deep-Learning-Based Fingerprinting Outdoor Positioning Scheme in Hybrid Networks

Tarekegn, Getaneh Berie; Juang, Rong‐Terng; Lin, Hsin‐Piao; Belay, Abebe; Munaye, Yirga Yayeh

doi:10.1109/jiot.2020.3024845

Cited by 18 publications

(5 citation statements)

References 39 publications

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“…On the basis of the most effective architectures selected from the previous chapter, in this article the authors examined the influence of the content and structure of input vectors of deep neural networks. Figures 14,15 The results obtained by the models trained on the vectors consisting of WiFi measurements or a combination of WiFi and LTE measurements reached the smallest RMSE localization errors for regression. For the indoor regression on floors 3rd, 4th, 5th, basement and the outdoor regression the differences between the largest and smallest RMSE localization From the visualization of the users' localization estimates by the deep learning algorithm presented in Figure 17, 18, 19 and 20 it can be observed that the number of determined position estimates is smaller than the number of positions determined by the GPS system.…”

Section: Radiolocation System Resultsmentioning

confidence: 92%

“…In the table I a summary of already described in this chapter and other published articles in which authors proposed radiolocalization fingerprinting systems supported with DL algorithms is presented. Outdoor localization [15] 60 × 60 m WiFi, LTE DNN 0.4 m -mean error [22] 100 × 100 m simulation RSS DNN…”

Section: Related Work Deep Learning Based Localization Using Signals ...mentioning

confidence: 99%

“…In existing research it is common to consider either indoor or outdoor localization separately with relatively small localization areas [12], [15]. Researchers also usually take measurements of only WiFi Received Signal Strength Indicator (RSSI) [8]- [10], [12], [16], [17].…”

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confidence: 99%

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International Journal of Electronics and Telecommunications

Urwan

Wysocka

Pietrzak

et al. 2022

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To improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application. The user's position is calculated with a trained neural network system's models. Additionally the influence of various number of measurements from LTE and WiFi networks in the input vector on the positioning accuracy was examined. From the results it can be seen that using hybrid deep learning algorithm with a radio signatures method can result in localization error 24.3 m and 1.9 m lower comparing respectively to the GPS system and standalone deep learning algorithm with a radio signatures method in indoor environment. What is more, the combination of LTE and WiFi signals measurement in an input vector results in better indoor and outdoor as well as floor classification accuracy and less positioning error comparing to the input vector consisting measurements from only LTE network or from only WiFi network.

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Section: Radiolocation System Resultsmentioning

confidence: 92%

Section: Related Work Deep Learning Based Localization Using Signals ...mentioning

confidence: 99%

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International Journal of Electronics and Telecommunications

Urwan

Wysocka

Pietrzak

et al. 2022

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“…This RNN-based research is a representative case where long-short term memory (LSTM) and gated recurrent unit (GRU) [ 41 , 42 , 43 , 44 ] are applied. These neural network-based methods are mainly studied to improve the location accuracy without distinguishing between fingerprint or TOF.…”

Section: Related Workmentioning

confidence: 99%

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Seong

Lee

Kim

et al. 2021

Sensors

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Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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“…In [25], LTE signal measurements were transformed into grayscale images of positions and the utilization of a deep residual network was learned. Moreover, a method of hierarchically combining support vector machine and long short‐term memory to use for coarse localization and fine localization, respectively, was introduced in [26].…”

Section: Introductionmentioning

confidence: 99%

Accurate Long‐Term Evolution/Wi‐Fi hybrid positioning technology for emergency rescue

Jeon

Han

et al. 2022

ETRI Journal

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It is critical to estimate the location using only Long‐Term Evolution (LTE) and Wi‐Fi information gathered by the user's smartphone and deployable for emergency rescue, regardless of whether the Global Positioning System is received. In this research, we used a vehicle to gather LTE and Wi‐Fi wireless signals over a large area for an extended period of time. After that, we used the learning technique to create a positioning database that included both collection and noncollection points. We presented a two‐step positioning algorithm that utilizes coarse localization to discover a rough location in a wide area rapidly and fine localization to estimate a particular location based on the coarse position. We confirmed our technology utilizing different sorts of devices in four regional types that are generally encountered: dense urban, urban, suburban, and rural. Results presented that our algorithm can satisfactorily achieve the target accuracy necessary in emergency rescue circumstances.

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D_FOPS: Deep-Learning-Based Fingerprinting Outdoor Positioning Scheme in Hybrid Networks

Cited by 18 publications

References 39 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Accurate Long‐Term Evolution/Wi‐Fi hybrid positioning technology for emergency rescue

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DFOPS: Deep-Learning-Based Fingerprinting Outdoor Positioning Scheme in Hybrid Networks

Cited by 18 publications

References 39 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Accurate Long‐Term Evolution/Wi‐Fi hybrid positioning technology for emergency rescue

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D_FOPS: Deep-Learning-Based Fingerprinting Outdoor Positioning Scheme in Hybrid Networks