Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System
The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), wh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/18/3946 |
| _version_ | 1828117887783534592 |
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| author | Faisal Jamil Do Hyeun Kim |
| author_facet | Faisal Jamil Do Hyeun Kim |
| author_sort | Faisal Jamil |
| collection | DOAJ |
| description | The navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha−beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha−beta filter with a learning module performed better than the traditional alpha−beta filter algorithm in terms of RMSE. |
| first_indexed | 2024-04-11T13:22:46Z |
| format | Article |
| id | doaj.art-d1a33b35f21b45318e2b39659382ad83 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:22:46Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d1a33b35f21b45318e2b39659382ad832022-12-22T04:22:10ZengMDPI AGSensors1424-82202019-09-011918394610.3390/s19183946s19183946Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation SystemFaisal Jamil0Do Hyeun Kim1Department of Computer Engineering, Jeju National University, Jejusi 63243, KoreaDepartment of Computer Engineering, Jeju National University, Jejusi 63243, KoreaThe navigation system has been around for the last several years. Recently, the emergence of miniaturized sensors has made it easy to navigate the object in an indoor environment. These sensors give away a great deal of information about the user (location, posture, communication patterns, etc.), which helps in capturing the user’s context. Such information can be utilized to create smarter apps from which the user can benefit. A challenging new area that is receiving a lot of attention is Indoor Localization, whereas interest in location-based services is also rising. While numerous inertial measurement unit-based indoor localization techniques have been proposed, these techniques have many shortcomings related to accuracy and consistency. In this article, we present a novel solution for improving the accuracy of indoor navigation using a learning to perdition model. The design system tracks the location of the object in an indoor environment where the global positioning system and other satellites will not work properly. Moreover, in order to improve the accuracy of indoor navigation, we proposed a learning to prediction model-based artificial neural network to improve the prediction accuracy of the prediction algorithm. For experimental analysis, we use the next generation inertial measurement unit (IMU) in order to acquired sensing data. The next generation IMU is a compact IMU and data acquisition platform that combines onboard triple-axis sensors like accelerometers, gyroscopes, and magnetometers. Furthermore, we consider a scenario where the prediction algorithm is used to predict the actual sensor reading from the noisy sensor reading. Additionally, we have developed an artificial neural network-based learning module to tune the parameter of alpha and beta in the alpha−beta filter algorithm to minimize the amount of error in the current sensor readings. In order to evaluate the accuracy of the system, we carried out a number of experiments through which we observed that the alpha−beta filter with a learning module performed better than the traditional alpha−beta filter algorithm in terms of RMSE.https://www.mdpi.com/1424-8220/19/18/3946indoor navigation systeminertial measurement unitartificial neural networkalpha–beta filtermotion trackina |
| spellingShingle | Faisal Jamil Do Hyeun Kim Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System Sensors indoor navigation system inertial measurement unit artificial neural network alpha–beta filter motion trackina |
| title | Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System |
| title_full | Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System |
| title_fullStr | Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System |
| title_full_unstemmed | Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System |
| title_short | Improving Accuracy of the Alpha–Beta Filter Algorithm Using an ANN-Based Learning Mechanism in Indoor Navigation System |
| title_sort | improving accuracy of the alpha beta filter algorithm using an ann based learning mechanism in indoor navigation system |
| topic | indoor navigation system inertial measurement unit artificial neural network alpha–beta filter motion trackina |
| url | https://www.mdpi.com/1424-8220/19/18/3946 |
| work_keys_str_mv | AT faisaljamil improvingaccuracyofthealphabetafilteralgorithmusinganannbasedlearningmechanisminindoornavigationsystem AT dohyeunkim improvingaccuracyofthealphabetafilteralgorithmusinganannbasedlearningmechanisminindoornavigationsystem |