A Fuzzy-Innovation-Based Adaptive Kalman Filter for Enhanced Vehicle Positioning in Dense Urban Environments
In this paper, a fuzzy-innovation based adaptive extended Kalman filter (FI-AKF)is proposed to improve the performance of the GNSS/INS fusion system, which is degradeddue to satellite signal cutoff and attenuation and inaccurate modeling in dense urbanenvironments. The information used for sensor fu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/19/5/1142 |
| _version_ | 1798043664304308224 |
|---|---|
| author | Rinara Woo Eun-Ju Yang Dae-Wha Seo |
| author_facet | Rinara Woo Eun-Ju Yang Dae-Wha Seo |
| author_sort | Rinara Woo |
| collection | DOAJ |
| description | In this paper, a fuzzy-innovation based adaptive extended Kalman filter (FI-AKF)is proposed to improve the performance of the GNSS/INS fusion system, which is degradeddue to satellite signal cutoff and attenuation and inaccurate modeling in dense urbanenvironments. The information used for sensor fusion is obtained from real-time kinematic (RTK),micro-electro-mechanical system based inertial measumrement unit (MEMS-IMU), and on-boarddiagnostics (OBD). The fuzzy logic system is proposed to adaptively update the measurementcovariance matrix of the RTK according to the position dilution of precision (PDOP), the numberof receivable satellites, and the innovation of the extended Kalman filter (EKF). In addition, thedriving state of the vehicle is defined as stop, straight run, left/right turn, and the like. To reduce theheading estimation error of the Kalman filter, the estimated heading is corrected according to thedriving state. Also, the measurement covariance matrices of IMU and OBD are applied adaptivelyconsidering the characteristics of each sensor according to the driving state. In order to analyze theperformance of the proposed FI-AKF positioning system in a dense urban environment, a computersimulation is performed. The proposed FI-AKF is compared to the performance of the existingextended Kalman filter and the innovation-based adaptive extended Kalman filter. In addition, weconduct a performance comparison experiment with a commercial positioning system in the field test.Through each experiment, it is confirmed that the proposed FI-AKF system has higher positioningperformance than the comparison positioning systems in a dense urban environment. |
| first_indexed | 2024-04-11T22:52:16Z |
| format | Article |
| id | doaj.art-eb6ccd2b5ee34ebfa11049c685ee9cae |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:52:16Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-eb6ccd2b5ee34ebfa11049c685ee9cae2022-12-22T03:58:33ZengMDPI AGSensors1424-82202019-03-01195114210.3390/s19051142s19051142A Fuzzy-Innovation-Based Adaptive Kalman Filter for Enhanced Vehicle Positioning in Dense Urban EnvironmentsRinara Woo0Eun-Ju Yang1Dae-Wha Seo2Center for Embedded Software Technology, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaCenter for Embedded Software Technology, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaSchool of Electronics Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, KoreaIn this paper, a fuzzy-innovation based adaptive extended Kalman filter (FI-AKF)is proposed to improve the performance of the GNSS/INS fusion system, which is degradeddue to satellite signal cutoff and attenuation and inaccurate modeling in dense urbanenvironments. The information used for sensor fusion is obtained from real-time kinematic (RTK),micro-electro-mechanical system based inertial measumrement unit (MEMS-IMU), and on-boarddiagnostics (OBD). The fuzzy logic system is proposed to adaptively update the measurementcovariance matrix of the RTK according to the position dilution of precision (PDOP), the numberof receivable satellites, and the innovation of the extended Kalman filter (EKF). In addition, thedriving state of the vehicle is defined as stop, straight run, left/right turn, and the like. To reduce theheading estimation error of the Kalman filter, the estimated heading is corrected according to thedriving state. Also, the measurement covariance matrices of IMU and OBD are applied adaptivelyconsidering the characteristics of each sensor according to the driving state. In order to analyze theperformance of the proposed FI-AKF positioning system in a dense urban environment, a computersimulation is performed. The proposed FI-AKF is compared to the performance of the existingextended Kalman filter and the innovation-based adaptive extended Kalman filter. In addition, weconduct a performance comparison experiment with a commercial positioning system in the field test.Through each experiment, it is confirmed that the proposed FI-AKF system has higher positioningperformance than the comparison positioning systems in a dense urban environment.http://www.mdpi.com/1424-8220/19/5/1142adaptive Kalman filterfuzzy logicinnovationsensor fusion |
| spellingShingle | Rinara Woo Eun-Ju Yang Dae-Wha Seo A Fuzzy-Innovation-Based Adaptive Kalman Filter for Enhanced Vehicle Positioning in Dense Urban Environments Sensors adaptive Kalman filter fuzzy logic innovation sensor fusion |
| title | A Fuzzy-Innovation-Based Adaptive Kalman Filter
for Enhanced Vehicle Positioning in Dense
Urban Environments |
| title_full | A Fuzzy-Innovation-Based Adaptive Kalman Filter
for Enhanced Vehicle Positioning in Dense
Urban Environments |
| title_fullStr | A Fuzzy-Innovation-Based Adaptive Kalman Filter
for Enhanced Vehicle Positioning in Dense
Urban Environments |
| title_full_unstemmed | A Fuzzy-Innovation-Based Adaptive Kalman Filter
for Enhanced Vehicle Positioning in Dense
Urban Environments |
| title_short | A Fuzzy-Innovation-Based Adaptive Kalman Filter
for Enhanced Vehicle Positioning in Dense
Urban Environments |
| title_sort | fuzzy innovation based adaptive kalman filter for enhanced vehicle positioning in dense urban environments |
| topic | adaptive Kalman filter fuzzy logic innovation sensor fusion |
| url | http://www.mdpi.com/1424-8220/19/5/1142 |
| work_keys_str_mv | AT rinarawoo afuzzyinnovationbasedadaptivekalmanfilterforenhancedvehiclepositioningindenseurbanenvironments AT eunjuyang afuzzyinnovationbasedadaptivekalmanfilterforenhancedvehiclepositioningindenseurbanenvironments AT daewhaseo afuzzyinnovationbasedadaptivekalmanfilterforenhancedvehiclepositioningindenseurbanenvironments AT rinarawoo fuzzyinnovationbasedadaptivekalmanfilterforenhancedvehiclepositioningindenseurbanenvironments AT eunjuyang fuzzyinnovationbasedadaptivekalmanfilterforenhancedvehiclepositioningindenseurbanenvironments AT daewhaseo fuzzyinnovationbasedadaptivekalmanfilterforenhancedvehiclepositioningindenseurbanenvironments |