High-sampled structural displacement estimation through the FIR filter-based two-stage fusion of high-sampled acceleration and temporally aliased low-sampled displacement measurements

This paper proposes an FIR filter-based two-stage fusion technique for high-sampled (HS) structural displacement estimation using HS acceleration and temporally aliased low-sampled (TLS) displacement measurements. First, the temporally aliased error in the TLS displacement measurement is estimated using the acceleration measurement and then eliminated to obtain an anti-aliased low-sampled (ALS) displacement. Next, a low-frequency displacement is estimated from the ALS displacement, and a high-frequency displacement is estimated from the HS acceleration measurement. Finally, the HS displacement is estimated by combining the estimated lowand high-frequency displacements. The proposed technique is also applied to estimate the HS structural displacement by fusing a vision camera and an accelerometer. An automated algorithm is proposed to estimate a scale factor for converting a translation in a pixel unit to a displacement in a length unit and to align measurements of two sensors using short-period HS acceleration and TLS vision measurements. The performance of the proposed technique was numerically and experimentally validated. A significant improvement in the displacement estimation accuracy was achieved compared to existing FIR filter-based techniques owing to the explicit elimination of the temporally aliased error.