Please use this identifier to cite or link to this item:
|Title: ||Interacting multiple model adaptive unscented Kalman filtering for navigation sensor fusion|
|Authors: ||D.-J. Jwo|
|Issue Date: ||2018-09-13T07:26:18Z
|Abstract: ||Abstract: The unscented Kalman filter (UKF) is adopted in the interacting multiple model (IMM) framework to deal with the system nonlinearity in navigation applications. The adaptive tuning system (ATS) is employed for assisting the unscented Kalman filter in the IMM framework, resulting in an interacting multiple model adaptive unscented Kalman filter (IMM-AUKF). Two models, a standard UKF and an adaptive UKF (AUKF), are used in the IMM for dynamically adjusting the process noise to enhance the estimation accuracy and tracking capability. Accuracy comparison on navigation sensor fusion for AUKF, IMM-UKF, and IMMAUKF approaches are presented. Furthermore, a performance measure referred to as the Instability Index (ISI) is introduced to evaluate the stability influenced by time-varying dynamics characteristics. Among the three approaches, the IMM-AUKF approach has the best overall positioning performance. Unlike the IMM-UKF, both IMM-AUKF and AUKF have equivalently good ISI values, indicating that positioning accuracies by the two methods are relatively reliable under the change of dynamics characteristics.
Interacting multiple model adaptive unscented Kalman filtering for navigation sensor fusion. Available from: https://www.researchgate.net/publication/285516738_Interacting_multiple_model_adaptive_unscented_Kalman_filtering_for_navigation_sensor_fusion [accessed Sep 13 2018].
|Relation: ||5(6) pp.3577-3586|
|Appears in Collections:||[通訊與導航工程學系] 期刊論文|
Files in This Item:
All items in NTOUR are protected by copyright, with all rights reserved.