scholarly journals CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1114 ◽  
Author(s):  
Feng Qin ◽  
Tao Zuo ◽  
Xing Wang
Keyword(s):  
Indoor Positioning ◽  
Location Estimation ◽  
Ease Of Use ◽  
Positioning System ◽  
Transmission Distance ◽  
Positioning Errors ◽  
The Mean ◽  
Validation Set ◽  
Indoor Positioning System ◽  
Wifi Fingerprint

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.

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

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

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.

scholarly journals Trilateration Method For Estimating Location in RSSI-Based Indoor Positioning System Using Zigbee Protocol

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Herryawan Pujiharsono ◽  
Duwi Utami ◽  
Rafina Destiarti Ainul
Keyword(s):  
Path Loss ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Positioning System ◽  
Observation Area ◽  
Current Location ◽  
Zigbee Protocol ◽  
The Mean ◽  
Unknown Node ◽  
Indoor Positioning System

Wireless network technology that is used today is developing rapidly because of the increasing need for location information of an object with high accuracy. Global Positioning System (GPS) is a technology to estimate the current location. Unfortunately, GPS has a disadvantage of low accuracy of 10 meters when used indoors. Therefore, it began to be developed with the concept of an indoor positioning system. This is a technology used to estimate the location of objects in a building by utilizing WSN (Wireless Sensor Network). The purpose of this study is to estimate the location of the unknown nodes in the lecturer room as an object and obtain the accuracy of the system being tested. The positioning process is based on the received signal strength (RSSI) on the unknown node using the ZigBee module. The trilateration method is used to estimate unknown node located at the observation area based on the signal strength received at the time of testing. The result shows that the path loss coefficient value at the observation area is 0.9836 and the Mean Square Error of the test is 1.251 meters, which indicates that the system can be a solution to the indoor GPS problem.

scholarly journals Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santosh Subedi ◽  
Jae-Young Pyun
Keyword(s):  
Estimation Error ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Reference Points ◽  
Ultra Wideband ◽  
Positioning System ◽  
Localization Method ◽  
Weighted Centroid Localization ◽  
Time Required ◽  
Indoor Positioning System

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

scholarly journals A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sajida Imran ◽  
Young-Bae Ko
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Location Based Services ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Positioning System ◽  
Local Fisher Discriminant Analysis ◽  
Indoor Positioning System

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

scholarly journals A Hidden Markov Model to Estimate the Time Dairy Cows Spend in Feeder Based on Indoor Positioning Data

2018 ◽  
Author(s):  
Matti Pastell ◽  
Frondelius Lilli
Keyword(s):  
Dairy Cows ◽  
State Transition ◽  
Viterbi Algorithm ◽  
Transition Probabilities ◽  
Wide Band ◽  
Indoor Positioning ◽  
Feeding Time ◽  
Positioning System ◽  
The Mean ◽  
Indoor Positioning System

The feeding time of dairy cows is linked with the health status of the animal and can be used to estimate daily feed intake together with other measurements. The aim of this study was to develop a model to measure the time a dairy cow spends at a feed bunk using an Ultra wide-band indoor positioning system.We measured the feeding behavior of 50 dairy cows during 7 days using Ubisense indoor positioning system and Insentec roughage feeders. We calculated the feeding (presence at the feeder) probability of the cow using logistic regression model with the distance to feed barrier as input and used the Viterbi algorithm to calculate the most likely state (feeding or not feeding) given state transition probabilities. The model was able to predict whether the cow was at the feeding trough or not with the accuracy of 97.6%, sensitivity 95.3% and specificity 97.9%. The model was also able to estimate the mean bout duration and the number of feeding bouts.

scholarly journals Platform and Algorithm Development for a RFID-Based Indoor Positioning System

2014 ◽  
Vol 02 (03) ◽  
pp. 279-291 ◽  
Author(s):  
Han Zou ◽  
Lihua Xie ◽  
Qing-Shan Jia ◽  
Hengtao Wang
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Positioning ◽  
Location Estimation ◽  
Support Vector ◽  
Rfid Tags ◽  
Positioning System ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Reference Tags ◽  
Indoor Positioning System

In recent years, developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location-Based Service (LBS) in indoor environment. Several advantages of Radio Frequency Identification (RFID) Technology, such as anti-interference, small, light and portable size of RFID tags, and its unique identification of different objects, make it superior to other wireless communication technologies for indoor positioning. However, certain drawbacks of existing RFID-based IPSs, such as high cost of RFID readers and active tags, as well as heavy dependence on the density of reference tags to provide the LBS, largely limit the application of RFID-based IPS. In order to overcome these drawbacks, we develop a cost-efficient RFID-based IPS by using cheaper active RFID tags and sensors. Furthermore, we also proposed three localization algorithms: Weighted Path Loss (WPL), Extreme Learning Machine (ELM) and integrated WPL-ELM. WPL is a centralized model-based approach which does not require any reference tags and provides accurate location estimation of the target effectively. ELM is a machine learning fingerprinting-based localization algorithm which can provide higher localization accuracy than other existing fingerprinting-based approaches. The integrated WPL-ELM approach combines the fast estimation of WPL and the high localization accuracy of ELM. Based on the experimental results, this integrated approach provides a higher localization efficiency and accuracy than existing approaches, e.g., the LANDMARC approach and the support vector machine for regression (SVR) approach.

scholarly journals Unsupervised Indoor Positioning System Based on Environmental Signatures

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 327
Author(s):  
Pan Feng ◽  
Danyang Qin ◽  
Min Zhao ◽  
Ruolin Guo ◽  
Teklu Berhane
Keyword(s):  
Dead Reckoning ◽  
Indoor Positioning ◽  
Convergence Time ◽  
Positioning System ◽  
Indoor Environments ◽  
Test Results ◽  
Positioning Errors ◽  
Localization And Mapping ◽  
Indoor Positioning System ◽  
Unconventional Method

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning.

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