Sequential Fusion for Multirate Multisensor Systems With Heavy-Tailed Noises and Unreliable Measurements

Liping Yan, Chenying Di, Q. M. Jonathan Wu, Yuanqing Xia

Abstract: The sequential fusion estimation for multirate multisensor dynamic systems with heavy-tailed noises and unreliable measurements is an important problem in dynamic system control. This work proposes a sequential fusion algorithm and a detection technique based on Student’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -distribution and the approximate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -filter. The performance of the proposed algorithm is analyzed and compared with the Gaussian Kalman filter-based sequential fusion and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -filter-based sequential fusion without detection technique. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective and robust to unreliable measurements. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -filter-based sequential fusion algorithm is shown to be the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm.