scholarly journals Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 982 ◽  
Author(s):  
Yue Liu ◽  
Rashmi Sharan Sinha ◽  
Shu-Zhi Liu ◽  
Seung-Hoon Hwang
Keyword(s):  
Deep Learning ◽  
Side Information ◽  
Success Probability ◽  
Dynamic Environment ◽  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Learning Classifier ◽  
Learning Classifiers

Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning.

scholarly journals Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4349
Author(s):  
Shu-Zhi Liu ◽  
Rashmi Sharan Sinha ◽  
Seung-Hoon Hwang
Keyword(s):  
Deep Learning ◽  
Spatial Clustering ◽  
Success Probability ◽  
Indoor Positioning ◽  
Noise Elimination ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Learning Classifier ◽  
Simulation Results ◽  
Elimination Scheme

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively.

scholarly journals Feature Learning for Fingerprint-Based Positioning in Indoor Environment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zengwei Zheng ◽  
Yuanyi Chen ◽  
Tao He ◽  
Lin Sun ◽  
Dan Chen
Keyword(s):  
Indoor Environment ◽  
Dynamic Environment ◽  
Feature Learning ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Heterogeneous Devices ◽  
Positioning Algorithm ◽  
Positioning Time ◽  
Fingerprint Feature

Recent years have witnessed a growing interest in using Wi-Fi received signal strength for indoor fingerprint-based positioning. However, previous study about this problem has primarily faced two main challenges. One is that positioning fingerprint feature using received signal strength is unstable due to heterogeneous devices and dynamic environment status, which will greatly degrade the positioning accuracy. Another is that some improved positioning fingerprint features will suffer the curse of dimensionality in online positioning. In this paper, we designed a novel positioning fingerprint feature using the segment similarity of Wi-Fi access points, considering both the received signal strength value and the Wi-Fi access point. Based on this designed fingerprint feature, we proposed a two-stage positioning algorithm for indoor fingerprint-based positioning. Experiment results indicate that our proposed positioning methodology can not only achieve better positioning performance but also consume less positioning time compared to three baseline methods.

scholarly journals Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Deep Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Learning Model ◽  
Signal Interference ◽  
Positioning System ◽  
Heat Maps ◽  
Positioning Systems ◽  
Localization Performance ◽  
Deep Learning Model

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Indoor Positioning Using WLAN Received Signal Strength

2004 ◽  
Vol 37 (7) ◽  
pp. 77-80
Author(s):  
Jaywon Chey ◽  
Jae Woong Chun ◽  
Suk Ja Kim ◽  
Jin Hyun Lee ◽  
Gyu-In Jee ◽  
...  
Keyword(s):  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength
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