Database Correlation Method for Multi-System Positioning

Kemppi, Paul; Nousiainen, Sami

doi:10.1109/vetecs.2006.1682948

Cited by 12 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, NN seems to perform satisfactorily and provides equally good results in scenarios with high density RSS radiomaps [ 19 ]. More complex deterministic algorithms are discussed in literature, such as the linear discriminant analysis [ 26 ] and the Database Correlation Method (DCM) [ 27 ]. They generally claim better localization accuracy but at a higher computational cost.…”

Section: Related Workmentioning

confidence: 99%

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Kanaris

Kokkinis

Liotta

et al. 2017

Sensors

View full text Add to dashboard Cite

Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.

show abstract

Section: Related Workmentioning

confidence: 99%

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Kanaris

Kokkinis

Liotta

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…This motivated us to propose a combination of deep learning and KNN methods to improve localisation in complex 3D environments. Since the main focus of this paper is on the deterministic positioning approach based on deep learning and K-nearest neighbours, more complex methods such as the database correlation method (DCM), linear discriminant analysis (LDA), and the k-anonymity method can be found in [56][57][58], respectively. The following subsection deals with existing research contributions related to deep learning.…”

Section: Deterministic Approachmentioning

confidence: 99%

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

El Boudani,

Dagiuklas,

Kanaris

et al. 2023

Electronics

View full text Add to dashboard Cite

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D.

show abstract

“…Several papers have described the potential of cellular positioning in general [7,9], as well as of fingerprinting-based cellular positioning [10][11][12][13][14]. A good overview on previous work on in-car positioning in general, also including GPS and local sensors, is given in [15], and an overview of cellular positioning specifically is given in [8].…”

Section: Previous Workmentioning

confidence: 99%

Integrated tracking and route classification for travel time estimation based on cellular network signalling data

Gundlegård¹,

Karlsson²

2020

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Database Correlation Method for Multi-System Positioning

Cited by 12 publications

References 6 publications

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Integrated tracking and route classification for travel time estimation based on cellular network signalling data

Contact Info

Product

Resources

About