1,721,332 research outputs found
Experimental evaluation of the precision of received signal strength based visible light positioning
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Indoor localization using mind evolutionary algorithm-based geomagnetic positioning and smartphone IMU sensors
Full text linkWith the pervasiveness and ubiquitous distribution of the magnetic field in indoor environments, indoor localization using magnetic positioning (MP) has attracted considerable attention. This work concentrates on the MP and pedestrian dead reckoning (PDR) method, and constructs a fusion system for smartphones using MP and PDR based on the extended Kalman filter (EKF). The mind evolutionary algorithm (MEA) is introduced to search for the optimal magnetic position based on a heuristic searching strategy, which uses the similartaxis and dissimilation for the evolutionary operation. In the PDR module, the acceleration characteristics of different walking patterns are analyzed and the corresponding features are extracted. The enhanced genetic algorithm-based extreme learning machine (EGA-ELM) is adopted to train these features and address the gait recognition problem of different walking patterns. Finally, to obtain a lightweight and high-precision fusion method, MEA-based MP is integrated with PDR based on the EKF. Extensive experiments are conducted to evaluate the proposed methods. The testing results showed that MEA-based MP can obtain a location error within 2.3 m and steps can be recognized with a mean accuracy of 95% when different users participate in testing. The positioning results after fusion with PDR reveal that the mean location error and root-mean-square error (RMSE) are 1.25 m and 1.53 m respectively, which outperforms the MP, PDR, MP and PDR fusion methods using improved particle filter (IPF) and genetic particle filter (GPF)
From MFN to SFN : performance prediction through machine learning
Full text linkIn the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R-2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97
Characterization of vegetation loss and impact on network performance at V-band frequencies
Full text linkWireless communication systems using millimeter-wave (mmWave) frequencies can be used for high-throughput applications such as fixed wireless access, where static line-of-sight links are used to provide internet connectivity. The directive antennas are typically mounted on building facades above street level. Therefore, the wireless links are mainly subject to attenuation due to atmospheric absorption, rain, and vegetation. In this letter, we present vegetation loss measurements at -band frequencies ranging from 50 to 75 GHz, using a spectrum analyzer-based channel sounder. Existing vegetation models, including the vegetation-dependent exponential decay (VED) model, are validated based on the measured vegetation loss. Furthermore, IEEE 802.11ad transceivers are used for the validation of the vegetation models, and to evaluate the influence of vegetation on the network performance via packet error rate (PER) and throughput measurements
Simultaneous WiFi ranging compensation and localization for indoor NLoS environments
Full text linkSmartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively
Metaheuristic Optimization of LED Locations for Visible Light Positioning Network Planning
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