2022/04/01 by Fengchi Zhu, Yulong Huang, Chao Xue +3 · 4 citations
Computer Science · Engineering · #Target Tracking and Data Fusion in Sensor Networks #Structural Health Monitoring Techniques #Fault Detection and Control Systems
paper · doi:10.1109/taes.2022.3164012
Existing robust state estimation methods are generally unable to distinguish model uncertainties (state outliers) from measurement outliers as they only exploit the current measurement. In this article, the measurements in a sliding window are, therefore, utilized to better distinguish them, and an adaptive method is embedded, leading to a sliding window variational outlier-robust Kalman filter based on Student’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -noise modeling. Target tracking simulations and experiments show that the tracking accuracy and consistency of the proposed filter are superior to those of the existing state-of-the-art outlier-robust methods thanks to the improved ability to identify the outliers but at a cost of greater computational burden.