  Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement
data, may contain a significant amount of random noise. Thus, in this study, we
investigated a noise-reduction method that combines multivariate singular
spectrum analysis (MSSA) with low-dimensional data representation. MSSA is a
state-space reconstruction technique that utilizes time-delay embedding, and
the low-dimensional representation is achieved by projecting data onto the
singular value decomposition (SVD) basis. The noise-reduction performance of
the proposed method for unsteady PSP data, i.e., the projected MSSA, is
compared with that of the truncated SVD method, one of the most employed
noise-reduction methods. The result shows that the projected MSSA exhibits
better performance in reducing random noise than the truncated SVD method.
Additionally, in contrast to that of the truncated SVD method, the performance
of the projected MSSA is less sensitive to the truncation rank. Furthermore,
the projected MSSA achieves denoising effectively by extracting smooth
trajectories in a state space from noisy input data. Expectedly, the projected
MSSA will be effective for reducing random noise in not only PSP measurement
data, but also various high-dimensional time-series data.
