Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations
An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide dista...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/24/8304 |
| _version_ | 1797500819933757440 |
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| author | Anirudh Chhabra Jashwanth Rao Venepally Donghoon Kim |
| author_facet | Anirudh Chhabra Jashwanth Rao Venepally Donghoon Kim |
| author_sort | Anirudh Chhabra |
| collection | DOAJ |
| description | An accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method. |
| first_indexed | 2024-03-10T03:09:20Z |
| format | Article |
| id | doaj.art-12858d72fc9245fdb6a1271552d57bca |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:09:20Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-12858d72fc9245fdb6a1271552d57bca2023-11-23T10:29:36ZengMDPI AGSensors1424-82202021-12-012124830410.3390/s21248304Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise SituationsAnirudh Chhabra0Jashwanth Rao Venepally1Donghoon Kim2Department of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USADepartment of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USADepartment of Aerospace Engineering & Engineering Mechanics, University of Cincinnati, Cincinnati, OH 45221, USAAn accurate and reliable positioning system (PS) is a significant topic of research due to its broad range of aerospace applications, such as the localization of autonomous agents in GPS-denied and indoor environments. The PS discussed in this work uses ultra-wide band (UWB) sensors to provide distance measurements. UWB sensors are based on radio frequency technology and offer low power consumption, wide bandwidth, and precise ranging in the presence of nominal environmental noise. However, in practical situations, UWB sensors experience varying measurement noise due to unexpected obstacles in the environment. The localization accuracy is highly dependent on the filtering of such noise, and the extended Kalman filter (EKF) is one of the widely used techniques. In varying noise situations, where the obstacles generate larger measurement noise than nominal levels, EKF cannot offer precise results. Therefore, this work proposes two approaches based on EKF: sequential adaptive EKF and piecewise adaptive EKF. Simulation studies are conducted in static, linear, and nonlinear scenarios, and it is observed that higher accuracy is achieved by applying the proposed approaches as compared to the traditional EKF method.https://www.mdpi.com/1424-8220/21/24/8304extended Kalman filteradaptive filteringdisturbed environmentmeasurement noise covariancenonlinear estimation |
| spellingShingle | Anirudh Chhabra Jashwanth Rao Venepally Donghoon Kim Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations Sensors extended Kalman filter adaptive filtering disturbed environment measurement noise covariance nonlinear estimation |
| title | Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations |
| title_full | Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations |
| title_fullStr | Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations |
| title_full_unstemmed | Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations |
| title_short | Measurement Noise Covariance-Adapting Kalman Filters for Varying Sensor Noise Situations |
| title_sort | measurement noise covariance adapting kalman filters for varying sensor noise situations |
| topic | extended Kalman filter adaptive filtering disturbed environment measurement noise covariance nonlinear estimation |
| url | https://www.mdpi.com/1424-8220/21/24/8304 |
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