1,714 research outputs found
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
RF Localization in Indoor Environment
In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained
Group-In: Group Inference from Wireless Traces of Mobile Devices
This paper proposes Group-In, a wireless scanning system to detect static or
mobile people groups in indoor or outdoor environments. Group-In collects only
wireless traces from the Bluetooth-enabled mobile devices for group inference.
The key problem addressed in this work is to detect not only static groups but
also moving groups with a multi-phased approach based only noisy wireless
Received Signal Strength Indicator (RSSIs) observed by multiple wireless
scanners without localization support. We propose new centralized and
decentralized schemes to process the sparse and noisy wireless data, and
leverage graph-based clustering techniques for group detection from short-term
and long-term aspects. Group-In provides two outcomes: 1) group detection in
short time intervals such as two minutes and 2) long-term linkages such as a
month. To verify the performance, we conduct two experimental studies. One
consists of 27 controlled scenarios in the lab environments. The other is a
real-world scenario where we place Bluetooth scanners in an office environment,
and employees carry beacons for more than one month. Both the controlled and
real-world experiments result in high accuracy group detection in short time
intervals and sampling liberties in terms of the Jaccard index and pairwise
similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under
Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The
content of this paper does not reflect the official opinion of the EU.
Responsibility for the information and views expressed therein lies entirely
with the authors. Proc. of ACM/IEEE IPSN'20, 202
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
์ฌ๋ฌผ์ธํฐ๋ท์ ์ํ ๋ฌด์ ์ค๋ด ์ธก์ ์๊ณ ๋ฆฌ์ฆ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2022.2. ๊น์ฑ์ฒ .์ค๋ด ์์น ๊ธฐ๋ฐ ์๋น์ค๋ ์ค๋งํธํฐ์ ์ด์ฉํ ์ค๋ด์์์ ๊ฒฝ๋ก์๋ด, ์ค๋งํธ ๊ณต์ฅ์์์ ์์ ๊ด๋ฆฌ, ์ค๋ด ๋ก๋ด์ ์์จ์ฃผํ ๋ฑ ๋ง์ ๋ถ์ผ์ ์ ๋ชฉ๋ ์ ์์ผ๋ฉฐ, ์ฌ๋ฌผ์ธํฐ๋ท ์์ฉ์๋ ํ์์ ์ธ ๊ธฐ์ ์ด๋ค. ๋ค์ํ ์์น ๊ธฐ๋ฐ ์๋น์ค๋ฅผ ๊ตฌํํ๊ธฐ ์ํด์๋ ์ ํํ ์์น ์ ๋ณด๊ฐ ํ์ํ๋ฉฐ, ์ ํฉํ ๊ฑฐ๋ฆฌ ๋ฐ ์์น๋ฅผ ์ถ์ ๊ธฐ์ ์ด ํต์ฌ์ ์ด๋ค. ์ผ์ธ์์๋ ์์ฑํญ๋ฒ์์คํ
์ ์ด์ฉํด์ ์์น ์ ๋ณด๋ฅผ ํ๋ํ ์ ์๋ค.
๋ณธ ํ์๋
ผ๋ฌธ์์๋ ์์ดํ์ด ๊ธฐ๋ฐ ์ธก์ ๊ธฐ์ ์ ๋ํด ๋ค๋ฃฌ๋ค. ๊ตฌ์ฒด์ ์ผ๋ก, ์ ํ์ ์ ํธ ์ธ๊ธฐ ๋ฐ ๋๋ฌ ์๊ฐ์ ์ด์ฉํ ์ ๋ฐํ ์ค๋ด ์์น ์ถ์ ์ ์ํ ์ธ ๊ฐ์ง ๊ธฐ์ ์ ๋ํด ๋ค๋ฃฌ๋ค. ๋จผ์ , ๋น๊ฐ์๊ฒฝ๋ก ํ๊ฒฝ์์์ ๊ฑฐ๋ฆฌ ์ถ์ ์ ํ๋๋ฅผ ํฅ์์์ผ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ ์ธก์์ ์ ํ๋๋ฅผ ํฅ์์ํค๋ ํ์ด๋ธ๋ฆฌ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์๋์ผ ๋ฐด๋ ๋์ญ์ ์ ํธ์ธ๊ธฐ๋ฅผ ๊ฐ์๋์ ์ธก์ ํ์ฌ ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ ์ธก์ ๊ธฐ๋ฒ์ ์ ์ฉํ ๋, ๊ฑฐ๋ฆฌ ์ถ์ ๋ถ ๋จ๊ณ๋ง์ ๋ฐ์ดํฐ ๊ธฐ๋ฐ ํ์ต์ ์ด์ฉํ ๊น์ ์ ๊ฒฝ๋ง ํ๊ท ๋ชจ๋ธ๋ก ๋์ฒดํ ๋ฐฉ์์ด๋ค. ์ ์ ํ ํ์ต๋ ๊น์ ํ๊ท ๋ชจ๋ธ์ ์ฌ์ฉ์ผ๋ก ๋น๊ฐ์๊ฒฝ๋ก ํ๊ฒฝ์์ ๋ฐ์ํ๋ ๊ฑฐ๋ฆฌ ์ถ์ ์ค์ฐจ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๊ฐ์์ํฌ ์ ์์ผ๋ฉฐ, ๊ฒฐ๊ณผ์ ์ผ๋ก ์์น ์ถ์ ์ค์ฐจ ๋ํ ๊ฐ์์์ผฐ๋ค. ์ ์ํ ๋ฐฉ๋ฒ์ ์ค๋ด ๊ด์ ์ถ์ ๊ธฐ๋ฐ ๋ชจ์์คํ์ผ๋ก ํ๊ฐํ์ ๋, ๊ธฐ์กด ๊ธฐ๋ฒ๋ค์ ๋นํด์ ์์น ์ถ์ ์ค์ฐจ๋ฅผ ์ค๊ฐ๊ฐ์ ๊ธฐ์ค์ผ๋ก 22.3% ์ด์ ์ค์ผ ์ ์์์ ๊ฒ์ฆํ๋ค. ์ถ๊ฐ์ ์ผ๋ก, ์ ์ํ ๋ฐฉ๋ฒ์ ์ค๋ด์์์ AP ์์น๋ณํ ๋ฑ์ ๊ฐ์ธํจ์ ํ์ธํ๋ค.
๋ค์์ผ๋ก, ๋ณธ ๋
ผ๋ฌธ์์๋ ๋น๊ฐ์๊ฒฝ๋ก์์ ๋จ์ผ ๋์ญ ์์ ์ ํธ์ธ๊ธฐ๋ฅผ ์ธก์ ํ์ ๋ ๋น๊ฐ์๊ฒฝ๋ก๊ฐ ๋ง์ ์ค๋ด ํ๊ฒฝ์์ ์์น ์ถ์ ์ ํ๋๋ฅผ ๋์ด๊ธฐ ์ํ ๋ฐฉ์์ ์ ์ํ๋ค. ๋จ์ผ ๋์ญ ์์ ์ ํธ์ธ๊ธฐ๋ฅผ ์ด์ฉํ๋ ๋ฐฉ์์ ๊ธฐ์กด์ ์ด์ฉ๋๋ ์์ดํ์ด, ๋ธ๋ฃจํฌ์ค, ์ง๋น ๋ฑ์ ๊ธฐ๋ฐ์์ค์ ์ฝ๊ฒ ์ ์ฉ๋ ์ ์๊ธฐ ๋๋ฌธ์ ๋๋ฆฌ ์ด์ฉ๋๋ค. ํ์ง๋ง ์ ํธ ์ธ๊ธฐ์ ๋จ์ผ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ์ ์ด์ฉํ ๊ฑฐ๋ฆฌ ์ถ์ ์ ์๋นํ ์ค์ฐจ๋ฅผ ์ง๋
์ ์์น ์ถ์ ์ ํ๋๋ฅผ ๊ฐ์์ํจ๋ค. ์ด๋ฌํ ๋ฌธ์ ์ ์์ธ์ ๋จ์ผ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ๋ก๋ ์ค๋ด์์์ ๋ณต์กํ ์ ํ ์ฑ๋ ํน์ฑ์ ๋ฐ์ํ๊ธฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์ค๋ด ์์น ์ถ์ ์ ์ํ ๋ชฉ์ ์ผ๋ก, ์ค์ฒฉ๋ ๋ค์ค ์ํ ๊ฒฝ๋ก ๊ฐ์ ๋ชจ๋ธ์ ์๋กญ๊ฒ ์ ์ํ๋ค. ์ ์ํ ๋ชจ๋ธ์ ๊ฐ์๊ฒฝ๋ก ๋ฐ ๋น๊ฐ์๊ฒฝ๋ก์์์ ์ฑ๋ ํน์ฑ์ ๊ณ ๋ คํ์ฌ ์ ์ฌ์ ์ธ ํ๋ณด ์ํ๋ค์ ์ง๋๋ค. ํ ์๊ฐ์ ์์ ์ ํธ ์ธ๊ธฐ ์ธก์ ์น์ ๋ํด ๊ฐ ๊ธฐ์ค ๊ธฐ์ง๊ตญ๋ณ๋ก ์ต์ ์ ๊ฒฝ๋ก์์ค ๋ชจ๋ธ ์ํ๋ฅผ ๊ฒฐ์ ํ๋ ํจ์จ์ ์ธ ๋ฐฉ์์ ์ ์ํ๋ค. ์ด๋ฅผ ์ํด ๊ธฐ์ง๊ตญ๋ณ ๊ฒฝ๋ก์์ค๋ชจ๋ธ ์ํ์ ์กฐํฉ์ ๋ฐ๋ฅธ ์ธก์ ๊ฒฐ๊ณผ๋ฅผ ํ๊ฐํ ์งํ๋ก์ ๋น์ฉํจ์๋ฅผ ์ ์ํ์๋ค. ๊ฐ ๊ธฐ์ง๊ตญ๋ณ ์ต์ ์ ์ฑ๋ ๋ชจ๋ธ์ ์ฐพ๋๋ฐ ํ์ํ ๊ณ์ฐ ๋ณต์ก๋๋ ๊ธฐ์ง๊ตญ ์์ ์ฆ๊ฐ์ ๋ฐ๋ผ ๊ธฐํ๊ธ์์ ์ผ๋ก ์ฆ๊ฐํ๋๋ฐ, ์ ์ ์๊ณ ๋ฆฌ์ฆ์ ์ด์ฉํ ํ์์ ์ ์ฉํ์ฌ ๊ณ์ฐ๋์ ์ต์ ํ์๋ค. ์ค๋ด ๊ด์ ์ถ์ ๋ชจ์์คํ์ ํตํ ๊ฒ์ฆ๊ณผ ์ค์ธก ๊ฒฐ๊ณผ๋ฅผ ์ด์ฉํ ๊ฒ์ฆ์ ์งํํ์์ผ๋ฉฐ, ์ ์ํ ๋ฐฉ์์ ์ค์ ์ค๋ด ํ๊ฒฝ์์ ๊ธฐ์กด์ ๊ธฐ๋ฒ๋ค์ ๋นํด ์์น ์ถ์ ์ค์ฐจ๋ฅผ ์ฝ 31% ๊ฐ์์์ผฐ์ผ๋ฉฐ ํ๊ท ์ ์ผ๋ก 1.92 m ์์ค์ ์ ํ๋๋ฅผ ๋ฌ์ฑํจ์ ํ์ธํ๋ค.
๋ง์ง๋ง์ผ๋ก FTM ํ๋กํ ์ฝ์ ์ด์ฉํ ์ค๋ด ์์น ์ถ์ ์๊ณ ๋ฆฌ์ฆ์ ๋ํด ์ฐ๊ตฌํ์๋ค. ์ค๋งํธํฐ์ ๋ด์ฅ ๊ด์ฑ ์ผ์์ ์์ดํ์ด ํต์ ์์ ์ ๊ณตํ๋ FTM ํ๋กํ ์ฝ์ ํตํ ๊ฑฐ๋ฆฌ ์ถ์ ์ ์ด์ฉํ์ฌ ์ค๋ด์์ ์ฌ์ฉ์์ ์์น๋ฅผ ์ถ์ ํ ์ ์๋ค. ํ์ง๋ง ์ค๋ด์ ๋ณต์กํ ๋ค์ค๊ฒฝ๋ก ํ๊ฒฝ์ผ๋ก ์ธํ ํผํฌ ๊ฒ์ถ ์คํจ๋ ๊ฑฐ๋ฆฌ ์ธก์ ์น์ ํธํฅ์ฑ์ ์ ๋ฐํ๋ค. ๋ํ ์ฌ์ฉํ๋ ๋๋ฐ์ด์ค์ ์ข
๋ฅ์ ๋ฐ๋ผ ์์์น ๋ชปํ ๊ฑฐ๋ฆฌ ์ค์ฐจ๊ฐ ๋ฐ์ํ ์์๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ค์ ํ๊ฒฝ์์ FTM ๊ฑฐ๋ฆฌ ์ถ์ ์ ์ด์ฉํ ๋ ๋ฐ์ํ ์ ์๋ ์ค์ฐจ๋ค์ ๊ณ ๋ คํ๊ณ ์ด๋ฅผ ๋ณด์ํ๋ ๋ฐฉ์์ ๋ํด ์ ์ํ๋ค. ํ์ฅ ์นผ๋ง ํํฐ์ ๊ฒฐํฉํ์ฌ FTM ๊ฒฐ๊ณผ๋ฅผ ์ฌ์ ํํฐ๋ง ํ์ฌ ์ด์๊ฐ์ ์ ๊ฑฐํ๊ณ , ๊ฑฐ๋ฆฌ ์ธก์ ์น์ ํธํฅ์ฑ์ ์ ๊ฑฐํ์ฌ ์์น ์ถ์ ์ ํ๋๋ฅผ ํฅ์์ํจ๋ค. ์ค๋ด์์์ ์คํ ๊ฒฐ๊ณผ ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ๊ฑฐ์น ์ธก์ ์น์ ํธํฅ์ฑ์ ์ฝ 44-65% ๊ฐ์์์ผฐ์ผ๋ฉฐ ์ต์ข
์ ์ผ๋ก ์ฌ์ฉ์์ ์์น๋ฅผ ์๋ธ๋ฏธํฐ๊ธ์ผ๋ก ์ถ์ ํ ์ ์์์ ๊ฒ์ฆํ๋ค.Indoor location-based services (LBS) can be combined with various applications such as indoor navigation for smartphone users, resource management in smart factories, and autonomous driving of robots. It is also indispensable for Internet of Things (IoT) applications. For various LBS, accurate location information is essential. Therefore, a proper ranging and positioning algorithm is important. For outdoors, the global navigation satellite system (GNSS) is available to provide position information. However, the GNSS is inappropriate indoors owing to the issue of the blocking of the signals from satellites. It is necessary to develop a technology that can replace GNSS in GNSS-denied environments. Among the various alternative systems, the one of promising technology is to use a Wi-Fi system that has already been applied to many commercial devices, and the infrastructure is in place in many regions.
In this dissertation, Wi-Fi based indoor localization methods are presented. In the specific, I propose the three major issues related to accurate indoor localization using received signal strength (RSS) and fine timing measurement (FTM) protocol in the 802.11 standard for my dissertation topics.
First, I propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. I replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error.
Next, I study on positioning algorithm that considering NLOS conditions for each APs, using single band RSS measurement. The single band RSS information is widely used for indoor localization because they can be easily implemented by using existing infrastructure like Wi-Fi, Blutooth, or Zigbee. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, I develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and NLOS states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, I formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, I apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92~m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09\% over the benchmark scenario.
Finally, I investigate accurate indoor tracking algorithm using FTM protocol in this dissertation.
By using the FTM ranging and the built-in sensors in a smartphone, it is possible to track the user's location in indoor. However, the failure of first peak detection due to the multipath effect causes a bias in the FTM ranging results in the practical indoor environment. Additionally, the unexpected ranging error dependent on device type also degrades the indoor positioning accuracy. In this study, I considered the factors of ranging error in the FTM protocol in practical indoor environment, and proposed a method to compensate ranging error. I designed an EKF-based tracking algorithm that adaptively removes outliers from the FTM result and corrects bias to increase positioning accuracy. The experimental results verified that the proposed algorithm reduces the average ofthe ranging bias by 43-65\% in an indoor scenarios, and can achieve the sub-meter accuracy in average route mean squared error of user's position in the experiment scenarios.Abstract i
Contents iv
List of Tables vi
List of Figures vii
1 INTRODUCTION 1
2 Hybrid Approach for Indoor Localization Using Received Signal Strength
of Dual-BandWi-Fi 6
2.1 Motivation 6
2.2 Preliminary 8
2.3 System model 11
2.4 Proposed Ranging Method 13
2.5 Performance Evaluation 16
2.5.1 Ray-Tracing-Based Simulation 16
2.5.2 Analysis of the Ranging Accuracy 21
2.5.3 Analysis of the Neural Network Structure 25
2.5.4 Analysis of Positioning Accuracy 26
2.6 Summary 29
3 Genetic Algorithm for Path Loss Model Selection in Signal Strength Based
Indoor Localization 31
3.1 Motivation 31
3.2 Preliminary 34
3.2.1 RSS-based Ranging Techniques 35
3.2.2 Positioning Technique 37
3.3 Proposed localization method 38
3.3.1 Localization Algorithm with Overlapped Multi-State Path Loss
Model 38
3.3.2 Localization with Genetic Algorithm-Based Search 41
3.4 Performance evaluation 46
3.4.1 Numerical simulation 50
3.4.2 Experimental results 56
3.5 Summary 60
4 Indoor User Tracking with Self-calibrating Range Bias Using FTM Protocol
62
4.1 Motivation 62
4.2 Preliminary 63
4.2.1 FTM ranging 63
4.2.2 PDR-based trajectory estimation 65
4.3 EKF design for adaptive compensation of ranging bias 66
4.4 Performance evaluation 69
4.4.1 Experimental scenario 69
4.4.2 Experimental results 70
4.5 Summary 75
5 Conclusion 76
Abstract (In Korean) 89๋ฐ
RSS-based wireless LAN indoor localization and tracking using deep architectures
Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73 m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35 m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2 m) under the same experiment environment.ECSEL Joint Undertaking ; European Union's H2020 Framework Programme (H2020/2014-2020) Grant ; National Authority TUBITA
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