Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones
Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/5/618 |
| _version_ | 1797412817194713088 |
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| author | Jan Grottke Jörg Blankenbach |
| author_facet | Jan Grottke Jörg Blankenbach |
| author_sort | Jan Grottke |
| collection | DOAJ |
| description | Due to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) based movements of the pedestrian dead reckoning with the absolute fingerprinting-based position estimations of Wireless Local Area Network (WLAN), Bluetooth (Bluetooth Low Energy—BLE), and magnetic field anomalies as well as a building model in real time. Thus, a step-based position estimation without knowledge of any start position was achieved. For this, a grid-based particle filter and a Bayesian filter approach were combined. Furthermore, various optimization methods were compared to weigh the different information sources within the sensor fusion algorithm, thus achieving high position accuracy. Although a particle filter was used, no particles move due to a novel grid-based particle interpretation. Here, the particles’ probability values change with every new information source and every stepwise iteration via a probability-map-based approach. By adjusting the weights of the individual measurement methods compared to a knowledge-based reference, the mean and the maximum position error were reduced by 31%, the RMSE by 34%, and the 95-percentile positioning errors by 52%. |
| first_indexed | 2024-03-09T05:08:42Z |
| format | Article |
| id | doaj.art-0e587881789940278f5d161c95a95104 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T05:08:42Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-0e587881789940278f5d161c95a951042023-12-03T12:51:19ZengMDPI AGElectronics2079-92922021-03-0110561810.3390/electronics10050618Evolutionary Optimization Strategy for Indoor Position Estimation Using SmartphonesJan Grottke0Jörg Blankenbach1Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, 52074 Aachen, GermanyGeodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, 52074 Aachen, GermanyDue to their distinctive presence in everyday life and the variety of available built-in sensors, smartphones have become the focus of recent indoor localization research. Hence, this paper describes a novel smartphone-based sensor fusion algorithm. It combines the relative inertial measurement unit (IMU) based movements of the pedestrian dead reckoning with the absolute fingerprinting-based position estimations of Wireless Local Area Network (WLAN), Bluetooth (Bluetooth Low Energy—BLE), and magnetic field anomalies as well as a building model in real time. Thus, a step-based position estimation without knowledge of any start position was achieved. For this, a grid-based particle filter and a Bayesian filter approach were combined. Furthermore, various optimization methods were compared to weigh the different information sources within the sensor fusion algorithm, thus achieving high position accuracy. Although a particle filter was used, no particles move due to a novel grid-based particle interpretation. Here, the particles’ probability values change with every new information source and every stepwise iteration via a probability-map-based approach. By adjusting the weights of the individual measurement methods compared to a knowledge-based reference, the mean and the maximum position error were reduced by 31%, the RMSE by 34%, and the 95-percentile positioning errors by 52%.https://www.mdpi.com/2079-9292/10/5/618indoor localizationsensor fusionsmartphone sensorshybrid positioning systemgrid modelBayesian filter |
| spellingShingle | Jan Grottke Jörg Blankenbach Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones Electronics indoor localization sensor fusion smartphone sensors hybrid positioning system grid model Bayesian filter |
| title | Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones |
| title_full | Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones |
| title_fullStr | Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones |
| title_full_unstemmed | Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones |
| title_short | Evolutionary Optimization Strategy for Indoor Position Estimation Using Smartphones |
| title_sort | evolutionary optimization strategy for indoor position estimation using smartphones |
| topic | indoor localization sensor fusion smartphone sensors hybrid positioning system grid model Bayesian filter |
| url | https://www.mdpi.com/2079-9292/10/5/618 |
| work_keys_str_mv | AT jangrottke evolutionaryoptimizationstrategyforindoorpositionestimationusingsmartphones AT jorgblankenbach evolutionaryoptimizationstrategyforindoorpositionestimationusingsmartphones |