Improved Rssi-Based Euclidean Distance Positioning Algorithm for Large and Dynamic Wlan Environments

Gansemer, Sebastian; Pueschel, Steffen; Frackowiak, Robert; Hakobyan, Syuzanna; Großmann, Uwe

doi:10.47839/ijc.9.1.696

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…The research [30] on the impact of forming learning datasets on the effectiveness of deep learning in radiolocation appli- cations shows that the positions of nearby reference points should be set with constant distance intervals. The RPs' grid distance was also examined in [33]. Thus, a constant distance value between adjacent RPs of the created radio map was used.…”

Section: B Indoor Scenariosmentioning

confidence: 99%

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: B Indoor Scenariosmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Urwan

Wysocka

Pietrzak

et al. 2022

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show abstract

“…The WKNN algorithm assigns weighting coefficients to the position coordinates of different RPs based on KNN, and the weight of each RP is usually set as the reciprocal of the Euclidian distance between RP’s and TP’s fingerprints. WKNN improves the positioning accuracy, and the implementation is simple [ 12 ]. However, room remains for improvement, which has inspired much research.…”

Section: Related Workmentioning

confidence: 99%

“…Among many indoor position estimation algorithms, those employing machine learning are the subject of much research. The K-nearest neighbors (KNN) algorithm, a popular machine learning method, was first introduced in positioning, and algorithms including WKNN, M-WKNN, and GK are based on it [ 11 , 12 , 13 , 14 ]. Other machine learning methods applied for indoor positioning include support vector machine (SVM) [ 15 , 16 ], k-means clustering [ 17 ], and deep neural networks [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

An RSS Transform—Based WKNN for Indoor Positioning

Zhou

Yang

Chen

2021

Sensors

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An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

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RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

Descamps-Vila

Pérez-Navarro

Conesa

2014

Connecting a Digital Europe Through Location and Place

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Location-based applications require knowing the user position constantly in order to find out and provide information about user's context. They use GPS signals to locate users, but unfortunately GPS location systems do not work in indoor environments. Therefore, there is a need of new methods that calculate the location of users in indoor environments using smartphone sensors. There are studies that propose indoor positioning systems but, as far as we know, they neither run on Android devices, nor can work in real environments. The goal of this chapter is to address that problem by presenting two methods that estimate the user position through a smartphone. The first method is based on euclidean distance and use Received Signal Strength (RSS) from WLAN Acces Points present in buildings. The second method uses sensor fusion to combine raw data of accelerometer and magnetometer inertial sensors. An Android prototype that implements both methods has been created and used to test both methods. The conclusions of the test are that RSS technique works efficiently in smartphones and achieves to estimate the position of users well enough to be used in real applications. On the contrary, the test results show that sensor fusion technique can be discarded due to bias errors and low frequency readings from accelerometers sensor.

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Improved Rssi-Based Euclidean Distance Positioning Algorithm for Large and Dynamic Wlan Environments

Cited by 9 publications

References 5 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

An RSS Transform—Based WKNN for Indoor Positioning

RSS and Sensor Fusion Algorithms for Indoor Location Systems on Smartphones

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