ArticleSensors (Basel, Switzerland)2021
Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 45 citations in OpenAlex.
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- Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems.Sensors (Basel, Switzerland) · 2022Article
- Landing System Development Based on Inverse Homography Range Camera Fusion (IHRCF).Sensors (Basel, Switzerland) · 2022Article
- Regularized Optimal Transport Based on an Adaptive Adjustment Method for Selecting the Scaling Parameters of Unscented Kalman Filters.Sensors (Basel, Switzerland) · 2022Article
- Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.Sensors (Basel, Switzerland) · 2021Article
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3 authors at 1 institution in 1 country.
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Abstract
In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.
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