Two-stage information filters for single and multiple sensors, and their square-root versions. (December 2018)

• Record Type: Journal Article
• Authors: Bharani Chandra, Kumar Pakki; Darouach, Mohamed
• Is Part Of: Automatica. Volume 98(2018)
• Page Start: 20
• Page End: 27
• Publication Date: 2018-12
• Subjects: Two-stage filters -- Information filters -- Multi-sensor state estimation; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2018.09.001
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Accurate states and unknown random bias estimation for well- and ill-conditioned systems are crucial for several applications. In this paper, a fusion of a two-stage Kalman filter and an information filter, and its extensions are considered to estimate the state variables and unknown random bias. Specifically, we propose four extensions of two-stage Kalman filters: two-stage information filter (TSIF), multi-sensor two-stage information filter (M-TSIF) and their square-root versions. The TSIF deals with single-sensor systems whereas the M-TSIF is capable to handle multi-sensor systems. For ill-conditioned systems, numerically stable square-root versions of TSIF and M-TSIF are developed. The performance of the proposed filters (along with the existing two-stage Kalman filter), for well- and ill-conditioned cases, is demonstrated on a quadruple-tank model.