Generalizing the unscented Kalman filter for state estimation

Quade Butler; Waleed Hilal; Brett Sicard; Youssef Ziada; S. Andrew Gadsden

doi:10.1117/12.2664227

14 June 2023 Generalizing the unscented Kalman filter for state estimation

Quade Butler, Waleed Hilal, Brett Sicard, Youssef Ziada, S. Andrew Gadsden

Proceedings Volume 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII; 1254703 (2023) https://doi.org/10.1117/12.2664227
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

The recent generalized unscented transform (GenUT) is formulated into a recursive Kalman filter framework. The GenUT constrains 2n + 1 sigma points and their weights to match the first four statistical moments of a probability distribution. The GenUT integrates well into the unscented Kalman filter framework, creating what we call the generalized unscented Kalman filter (GUKF). The measurement update equations for the skewness and kurtosis are derived within. Performance of the GUKF is compared to the UKF under two studies: noise described by a Gaussian distribution and noise described by a uniform distribution. The GUKF achieves lower errors in state estimation when the UKF uses the heuristic tuning parameter κ = 3 − n. It is also stated that when the parameter κ is tuned to an optimal value, the UKF performs identically to the GUKF. The advantage here is that GUKF requires no such tuning.

Conference Presentation

Citation Download Citation

Quade Butler, Waleed Hilal, Brett Sicard, Youssef Ziada, and S. Andrew Gadsden "Generalizing the unscented Kalman filter for state estimation", Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 1254703 (14 June 2023); https://doi.org/10.1117/12.2664227

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