scholarly journals Kalman Filter-Based Hybrid Indoor Position Estimation Technique in Bluetooth Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Fazli Subhan ◽  
Halabi Hasbullah ◽  
Khalid Ashraf
Keyword(s):  
Kalman Filter ◽  
Position Estimation ◽  
Propagation Model ◽  
Radio Propagation ◽  
Estimation Accuracy ◽  
Hybrid Technique ◽  
Estimation Technique ◽  
Euclidian Distance ◽  
Radio Propagation Model ◽  
Distance Formula

This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration of fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio propagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and trilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it uses an online phase of fingerprinting and calculates nearest neighbors (NN) of the target node, while in the second stage it uses trilateration approach to estimate the coordinate without the use of radio propagation model. The distance between calculated NN and detective access points (AP) is estimated using Euclidian distance formula. Thus, distance between NN and APs provides radii for trilateration approach. Therefore, the position estimation accuracy compared to the lateration approach is better. Kalman filter is used to further enhance the accuracy of the estimated position. Simulation and experimental results validate the performance of proposed hybrid technique and improve the accuracy up to 53.64% and 25.58% compared to lateration and fingerprinting approaches, respectively.

Research on Impact of Radio Irregularity on Self-Localization of WSN

2011 ◽  
Vol 474-476 ◽  
pp. 2161-2166 ◽  
Author(s):  
Jia Zhang ◽  
Hai Yan Zhang ◽  
Jin Na Lv ◽  
Li Qiang Yin
Keyword(s):  
Propagation Model ◽  
Radio Propagation ◽  
Coarse Grained ◽  
Wireless Sensor ◽  
Position Information ◽  
Localization Algorithms ◽  
Radio Propagation Model ◽  
Radio Irregularity ◽  
Almost All ◽  
The Impact

Localization is a vital foundation work in Wireless Sensor Network (WSN). Almost all of location algorithms at present need the position information of reference nodes to locate the unknown nodes. But most of algorithms assume an idealistic radio propagation model that is far from the reality. This will lead to obvious difference compared with real localization of WSN. In this paper we investigate the impact of radio irregularity on the localization algorithms performance in WSN. We introduce the Radio Irregularity Model (RIM) which is established upon empirical data. With this model, this paper analyzes the impact of radio irregularity on localization algorithms. We compare three typical coarse-grained localization algorithms: APIT, Centroid and DV-HOP in simulated realistic settings. Our experimental results show that radio irregularity has a significant impact on some main evaluation aspects of localization algorithms. Some interesting phenomena is worthy of further study.

scholarly journals MapFuse: Complete and Realistic 3D Modelling

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Michiel Aernouts ◽  
Ben Bellekens ◽  
Maarten Weyn
Keyword(s):  
Propagation Model ◽  
Radio Propagation ◽  
Initial Model ◽  
Registration Errors ◽  
Environment Model ◽  
Robust Model ◽  
Vision Sensors ◽  
Volumetric Model ◽  
Radio Propagation Model ◽  
Slam Algorithm

Validating a 3D indoor radio propagation model that simulates the signal strength of a wireless device can be a challenging task due to an incomplete or a faulty environment model. In this paper, we present a novel method to simulate a complete indoor environment that can be used for evaluating a radio propagation model efficiently. In order to obtain a realistic and robust model of the full environment, the OctoMap framework is applied. The system combines the result of a SLAM algorithm and secondly a simple initial model of the same environment in a probabilistic way. Due to this approach, sensor noise and accumulated registration errors are minimised. Furthermore, in this article, we evaluate the merging approach with two SLAM algorithms, three vision sensors, and four datasets, of which one is publicly available. As a result, we have created a complete volumetric model by merging an initial model of the environment with the result of RGB-D SLAM based on real sensor measurements.

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