scholarly journals SICD: Novel Single-Access-Point Indoor Localization Based on CSI-MIMO with Dimensionality Reduction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1325
Author(s):  
Yunwei Zhang ◽  
Weigang Wang ◽  
Chendong Xu ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Indoor Localization ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Location Based Services ◽  
Localization Accuracy ◽  
Single Access ◽  
Time Frequency ◽  
High Data ◽  
Log Normal

With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

scholarly journals A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 875 ◽  
Author(s):  
Xiaochao Dang ◽  
Xiong Si ◽  
Zhanjun Hao ◽  
Yaning Huang
Keyword(s):  
Indoor Localization ◽  
Rapid Development ◽  
Location Based Services ◽  
Signal Interference ◽  
Indoor Environments ◽  
Phase Information ◽  
Localization Accuracy ◽  
Localization Method ◽  
Indoor Location ◽  
Conference Room

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

scholarly journals DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 133 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Sangjoon Park ◽  
Yongwan Park
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Ensemble Classifier ◽  
Location Based Services ◽  
Magnetic Data ◽  
Neural Network Ensemble ◽  
Localization Accuracy ◽  
Mean Error ◽  
The Impact

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

scholarly journals Implementation and Evaluation of WLAN 802.11ac for Residential Networks in NS-3

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Andy Bubune Amewuda ◽  
Ferdinand Apietu Katsriku ◽  
Jamal-Deen Abdulai
Keyword(s):  
Wireless Lan ◽  
Multiple Input Multiple Output ◽  
Access Point ◽  
Operating Conditions ◽  
Network Simulator ◽  
High Data ◽  
Delay Jitter ◽  
Combined Features ◽  
5 Ghz ◽  
Very High

Wi-Fi has been an amazingly successful technology. Its success may be attributed to the fact that, despite the significant advances made in technology over the last decade, it has remained backward compatible. 802.11ac is the latest version of the wireless LAN (WLAN) standard that is currently being adopted, and it promises to deliver very high throughput (VHT), operating at the 5 GHz band. In this paper, we report on an implementation of 802.11ac wireless LAN for residential scenario based on the 802.11ax task group scenario document. We evaluate the 802.11ac protocol performance under different operating conditions. Key features such as modulation coding set (MCS), frame aggregation, and multiple-input multiple-output (MIMO) were investigated. We also evaluate the average throughput, delay, jitter, optimum range for goodput, and effect of station (STA) density per access point (AP) in a network. ns-3, an open source network simulator with features supporting 802.11ac, was used to perform the simulation. Results obtained indicate that very high data rates are achievable. The highest data rate, the best mean delay, and mean jitter are possible under combined features of 802.11ac (MIMO and A-MPDU).

scholarly journals Indoor Localization Using Bayesian Filter

2019 ◽  
Author(s):  
Karthik Muthineni ◽  
Attaphongse Taparugssanagorn
Keyword(s):  
Communication Technology ◽  
Indoor Localization ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Ambient Intelligent ◽  
Unknown Node ◽  
Visualization Of Data ◽  
Log Normal ◽  
Environment Parameters

Ambient Intelligent (AmI) Wireless Sensor Networks (WSN) provide intelligent services based on user and environment data obtained by sensors. Such networks are developed to give environmental monitoring and indoor localization services. In this work, Zigbee which is a wireless communication technology is used for localization based on Received Signal Strength Indicator (RSSI) method. In practice, Extended Kalman Filter (EKF) is adapted to filter RSSI values influenced by multi-path fading and noise. Log-Normal Shadowing Method (LNSM) together with the Trilateration method was implemented to locate the position of the unknown node or entity. In addition, Cramer Rao Lower Bound (CRLB) is derived for the position estimation, that can be used to evaluate the performance of the system in terms of localization accuracy. Along with indoor localization, the deployed WSN could also monitor environment parameters like temperature and humidity surrounding entity using Digital Humidity and Temperature (DHT11) sensor. Using Zigbee location coordinates of entity and environment parameters are transmitted to remote desktop where visualization of data is done using Matrix Laboratory (MATLAB).

scholarly journals An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

2020 ◽  
Vol 9 (4) ◽  
pp. 261
Author(s):  
Fan Xu ◽  
Xuke Hu ◽  
Shuaiwei Luo ◽  
Jianga Shang
Keyword(s):  
Indoor Localization ◽  
Attenuation Factor ◽  
Access Point ◽  
Location Estimation ◽  
Signal Attenuation ◽  
Distance Ratio ◽  
Localization Accuracy ◽  
Access Points ◽  
Attenuation Parameter ◽  
The Impact

Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

scholarly journals SP-Loc: A crowdsourcing fingerprint based shop-level indoor localization algorithm integrating shop popularity without the indoor map

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881563 ◽  
Author(s):  
Jie Wei ◽  
Fang Zhao ◽  
Haiyong Luo
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Location Based Services ◽  
Shopping Mall ◽  
Time Stamp ◽  
Localization Algorithm ◽  
Variation Pattern ◽  
Localization Accuracy ◽  
User Location

With the development of indoor localization technology, the location-based services such as product advertising recommendation in the shopping mall attract widespread attention, as precise user location significantly improves the efficiency of advertising push and brings broader profits. However, most of the Wi-Fi-based indoor localization approaches requiring professionals to deploy expensive beacon devices and intensively collect fingerprints in each location grid, which severely limits its extensive promotion. We introduce a zero-cost indoor localization algorithm utilizing crowdsourcing fingerprints to obtain the shop recognition where the user is located. Naturally utilizing the Wi-Fi, GPS, and time-stamp fingerprints collected from the smartphone when user paid as the crowdsourcing fingerprint, we avoid the requirement for indoor map and get rid of both devices cost and manual signal collecting process. Moreover, a shop-level hierarchical indoor localization framework is proposed, and high robustness features based on Wi-Fi sequences variation pattern in the same shop analysis are designed to avoid the received signal strength fluctuations. Besides, we also pay more attention to mine the popularity properties of shops and explore GPS features to improve localization accuracy in the Wi-Fi absence situation effectively. Massive experiments indicate that SP-Loc achieves more than 93% localization accuracy.

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